Quick description¶
Analysis of AOU trends versus Age trends. Partly copied from 25-03-28-figures-for-storyline-v2. Modified to do the analysis on the 1972-2013 time period (instead of 1986-2009)
Import modules¶
In [1]:
%%time
%load_ext memory_profiler
#___________________________
# basics
import datetime
import os, glob, sys, gc
# import warnings
# warnings.filterwarnings('ignore', '.*invalid value encountered in true_divide.*', )
#___________________________
# To follow computations
from dask.diagnostics import ProgressBar
pbar = ProgressBar(minimum=10)
pbar.register()
#pbar.unregister()
#___________________________
# xarray numpy...
import numpy as np
import xarray as xr
xr.set_options(keep_attrs=True)
from fast_histogram import histogram2d
from scipy import stats
#___________________________
# To plot
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy
import cmcrameri
import tol_colors
plt.figure()
mpl.rcParams['savefig.dpi'] = 300
mpl.rcParams['figure.dpi'] = 200 # dpi for figure shonw in the notebook (ie. size of the figure)
#___________________________
# Extra
import pickle
Starters¶
In [2]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Starters')
print('################################')
dirout = '25-05-05-figures-for-storyline-v3/'
if not os.path.isdir(dirout) : os.mkdir(dirout)
dirshared = '/mnt/reef-ns1002k/daco/MY_JUPYTER_NOTEBOOKS/OceanICU/SHARED-DATAS/'
netcdfdir = dirout+'netcdf_files/'
if not os.path.isdir(netcdfdir) : os.mkdir(netcdfdir)
sys.stdout.echo = open(dirout+'stdout.txt', 'w')
sys.stderr.echo = open(dirout+'stderr.txt', 'w')
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Main parameters¶
In [3]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Main parameters')
print('################################')
cm2in = 1/2.54 # to convert from cm to inches
# Set custom font sizes
plt.rcParams['font.size'] = 8 # Default font size: 10.0
plt.rcParams['axes.titlesize'] = 8 # Default axes title font size: large
plt.rcParams['axes.labelsize'] = 6 # Default axes label font size: large
plt.rcParams['xtick.labelsize'] = 6 # Default tick labels font size: medium
plt.rcParams['ytick.labelsize'] = 6 # Default tick labels font size: medium
plt.rcParams['legend.fontsize'] = 6 # Default legend font size: medium
plt.rcParams['figure.titlesize'] = 10 # Default figure title font size: large
plt.rcParams['lines.linewidth'] = 1.
plt.rcParams['grid.linewidth'] = .5
plt.rcParams['axes.linewidth'] = .5
plt.rcParams['xtick.major.width'] = .5
plt.rcParams['xtick.minor.width'] = .5
plt.rcParams['ytick.major.width'] = .5
plt.rcParams['ytick.minor.width'] = .5
kwopends=dict(use_cftime=True, decode_times=None,
decode_cf=True, decode_coords=True)
kwopenmfds = dict(combine='by_coords', parallel=True,
use_cftime=True, decode_times=None,
decode_cf=True, decode_coords=True)
rename_dict = {
"x": "i",
"y": "j",
"lat": "latitude",
"lon": "longitude",
"nav_lat": "latitude",
"nav_lon": "longitude",
'lev': 'depth',
'deptht': 'depth',
'olevel': 'depth',
"Depth":"depth"
}
# esm_color_dict = {
# 'MPI-ESM1-2-LR': tol_colors.tol_cset(colorset="bright").blue,
# 'ACCESS-ESM1-5': tol_colors.tol_cset(colorset="bright").red,
# 'IPSL-CM6A-LR' : tol_colors.tol_cset(colorset="bright").green,
# 'MIROC-ES2L' : tol_colors.tol_cset(colorset="bright").yellow,
# 'NorESM2-LM' : tol_colors.tol_cset(colorset="bright").cyan
# }
esm_color_dict = {
'MPI-ESM1-2-LR': cmcrameri.cm.batlowS.colors[0],
'ACCESS-ESM1-5': cmcrameri.cm.batlowS.colors[1],
'IPSL-CM6A-LR' : cmcrameri.cm.batlowS.colors[2],
'MIROC-ES2L' : cmcrameri.cm.batlowS.colors[3],
'NorESM2-LM' : cmcrameri.cm.batlowS.colors[4]
}
esm_name_dict = {
'MPI-ESM1-2-LR': 'MPI-ESM1.2-LR',
'ACCESS-ESM1-5': 'ACCESS-ESM1.5',
'IPSL-CM6A-LR' : 'IPSL-CM6A-LR' ,
'MIROC-ES2L' : 'MIROC-ES2L' ,
'NorESM2-LM' : 'NorESM2-LM' ,
'CanESM5' : 'CanESM5' ,
'CNRM-ESM2-1' : 'CNRM-ESM2-1' ,
'GFDL-ESM4' : 'GFDL-ESM4' ,
'UKESM1-0-LL' : 'UKESM1-0-LL'
}
picontrol_refyear_dict = {
'MPI-ESM1-2-LR': 1850,
'ACCESS-ESM1-5': 161,
'IPSL-CM6A-LR' : 1910,
'CanESM5' : 5201,
'MIROC-ES2L' : 1850,
'NorESM2-LM' : 1600,
'CNRM-ESM2-1' : 1850,
'GFDL-CM4' : 101,
'GFDL-ESM4' : 101,
'UKESM1-0-LL' : 2250
}
#--------------------
# Dictionnaries for ESGF
#--------------------
variant_dict = {
'MPI-ESM1-2-LR': 'r1i1p1f1',
'ACCESS-ESM1-5': 'r1i1p1f1',
'IPSL-CM6A-LR' : 'r1i1p1f1',
'CanESM5' : 'r1i1p1f1',
'MIROC-ES2L' : 'r1i1p1f2',
'CNRM-ESM2-1' : 'r1i1p1f2',
'GFDL-CM4' : 'r1i1p1f1',
'GFDL-ESM4' : 'r1i1p1f1',
'UKESM1-0-LL' : 'r1i1p1f2'
}
version_dict = {
'piControl': {
'thetao':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
},
'so':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
},
'o2':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
}
},
'historical': {
'thetao':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20180803',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20181206',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20190726',
'UKESM1-0-LL' : 'v20190627'
},
'so':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20180803',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20181206',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20190726',
'UKESM1-0-LL' : 'v20190627'
},
'o2':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20180803',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20181206',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20190726',
'UKESM1-0-LL' : 'v20190627'
},
'areacello':{
'MPI-ESM1-2-LR': 'latest',
'ACCESS-ESM1-5': 'latest',
'IPSL-CM6A-LR' : 'latest',
'CanESM5' : 'latest',
'MIROC-ES2L' : 'latest',
'CNRM-ESM2-1' : 'latest',
'GFDL-CM4' : 'latest',
'GFDL-ESM4' : 'latest',
'UKESM1-0-LL' : 'v20190705'
}
},
'ssp585': {
'thetao':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
},
'so':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
},
'o2':{
'MPI-ESM1-2-LR': 'v20190710',
'ACCESS-ESM1-5': 'v20191115',
'IPSL-CM6A-LR' : 'v20190903',
'CanESM5' : 'v20190429',
'MIROC-ES2L' : 'v20190823',
'CNRM-ESM2-1' : 'v20191021',
'GFDL-CM4' : 'v20180701',
'GFDL-ESM4' : 'v20180701',
'UKESM1-0-LL' : 'v20190726'
}
}
}
grid_dict = {
'thetao':{
'MPI-ESM1-2-LR': 'gn',
'ACCESS-ESM1-5': 'gn',
'IPSL-CM6A-LR' : 'gn',
'CanESM5' : 'gn',
'MIROC-ES2L' : 'gn',
'CNRM-ESM2-1' : 'gn',
'GFDL-CM4' : 'gr',
'GFDL-ESM4' : 'gr',
'UKESM1-0-LL' : 'gn'
},
'so':{
'MPI-ESM1-2-LR': 'gn',
'ACCESS-ESM1-5': 'gn',
'IPSL-CM6A-LR' : 'gn',
'CanESM5' : 'gn',
'MIROC-ES2L' : 'gn',
'CNRM-ESM2-1' : 'gn',
'GFDL-CM4' : 'gr',
'GFDL-ESM4' : 'gr',
'UKESM1-0-LL' : 'gn'
},
'o2':{
'MPI-ESM1-2-LR': 'gn',
'ACCESS-ESM1-5': 'gn',
'IPSL-CM6A-LR' : 'gn',
'CanESM5' : 'gn',
'MIROC-ES2L' : 'gn',
'CNRM-ESM2-1' : 'gn',
'GFDL-CM4' : 'gr',
'GFDL-ESM4' : 'gr',
'UKESM1-0-LL' : 'gn'
},
'areacello':{
'MPI-ESM1-2-LR': 'gn',
'ACCESS-ESM1-5': 'gn',
'IPSL-CM6A-LR' : 'gn',
'CanESM5' : 'gn',
'MIROC-ES2L' : 'gn',
'CNRM-ESM2-1' : 'gn',
'GFDL-CM4' : 'gr',
'GFDL-ESM4' : 'gr',
'UKESM1-0-LL' : 'gn'
}
}
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Define some functions¶
Preparation of data¶
In [4]:
def shift_180_lon(zwda, verbose=False):
if verbose: print("func: shift_180_lon")
try:
if not np.nanmin(zwda['longitude']) < -150:
zwda['longitude'] = (zwda['longitude'] + 180) % 360 - 180
addtxt=str(datetime.datetime.now())+' shift_180_lon to get longitude from -180 to 180'
try: zwda.attrs['history'] = addtxt + ' ; '+zwda.attrs['history']
except: zwda.attrs['history'] = addtxt
#
except: print('WARNING! longitude likely not shifted')
return zwda
#
def rename_vars_dims_coords(ds, rename_dict, verbose=False):
"""
Renames variables, dimensions, and coordinates in an xarray Dataset according to the provided rename dictionary.
Parameters:
-----------
ds : xr.Dataset
The xarray Dataset to be renamed.
rename_dict : Dict[str, str]
Dictionary containing the variable, dimension, or coordinate names to be renamed.
The keys represent the original names, and the values represent the new names.
verbose : bool, optional
If True, prints the function name at the start and end of execution (default is False).
Returns:
--------
xr.Dataset
A new xarray Dataset with variables, dimensions, and coordinates renamed according to the rename dictionary.
Example:
--------
import xarray as xr
data = {'temp': ([], [0]), 'sali': ([], [1])}
coords = {'time': [0]}
ds = xr.Dataset(data, coords)
renamed_ds = rename_vars_dims_coords(ds, {'temp': 'temperature', 'sali': 'salinity'})
Dependencies:
-------------
xarray
"""
if verbose: print('func: rename_vars_dims_coords')
for old_name, new_name in rename_dict.items():
if (old_name in ds.variables) | (old_name in ds.dims) | (old_name in ds.coords):
ds = ds.rename({old_name: new_name})
#
if verbose: print('endfunc')
return ds
#
def split_coords_dimensions(ds, verbose=False):
"""
Splits the latitude, longitude, and depth dimensions and coordinates of an xarray dataset into separate variables,
updates their names, and assigns them back to the dataset.
Parameters:
-----------
ds : xr.Dataset
The xarray Dataset to be updated.
verbose : bool, optional
If True, prints the function name at the start and end of execution (default is False).
Returns:
--------
xr.Dataset
A new xarray Dataset with the latitude, longitude, and depth dimensions and coordinates split into separate variables
and reassigned to the original dataset.
Example:
--------
import xarray as xr
data = {'temp': ([0, 1, 2], [0, 1]), 'sali': ([0, 1, 2], [0, 1])}
coords = {'latitude': [0, 1, 2], 'longitude': [0, 1], 'depth': [0, 1, 2]}
ds = xr.Dataset(data, coords)
updated_ds = split_coords_dimensions(ds)
Dependencies:
-------------
xarray
"""
if verbose: print('func: split_coords_dimensions')
new_coords = {}
new_coords2 = {}
new_dims = {}
dim_name_dict = dict(latitude='j', longitude='i', depth='k')
dimschanged = []
for name, coord in ds.coords.items():
if name in ds.dims and name in ["latitude", "longitude", "depth"]:
new_coords[name + "_coord"] = coord
new_dims[name] = dim_name_dict[name]
new_coords2[name + "_coord"] = name
dimschanged.append(name)
if verbose: print('endfunc')
for name in ['k', 'j', 'i']:
if name in ds.coords: dimschanged.append(name)
#
return ds.assign_coords(new_coords).rename_dims(new_dims).drop_vars(dimschanged).rename(new_coords2)
#
Others¶
In [5]:
def nan_helper(y):
"""Helper to handle indices and logical indices of NaNs.
Input:
- y, 1d numpy array with possible NaNs
Output:
- nans, logical indices of NaNs
- index, a function, with signature indices= index(logical_indices),
to convert logical indices of NaNs to 'equivalent' indices
Example:
>>> # linear interpolation of NaNs
>>> nans, x= nan_helper(y)
>>> y[nans]= np.interp(x(nans), x(~nans), y[~nans])
nb: y[~nans] values of y that are not nans
x(~nans) indexes of y that are not nans
"""
return np.isnan(y), lambda z: z.nonzero()[0]
#
def get_esgf_dataset_filepaths(variable, sourceID, experimentID,
freq='mon', grid='g*', version='latest',
variant='r1i1p1f1',
mipera = 'CMIP6', diresgf='/mnt/reef-ns1002k-esgf/', verbose=False, **kwargs):
"""
Returns the filepaths of the remote netCDF files corresponding to the specified dataset of the Earth System
Grid Federation (ESGF) data portal on NIRD.
Parameters:
-----------
variable : str
Variable to search for on ESGF data portal.
sourceID : str
Name of the data source on the ESGF data portal.
experimentID : str
Name of the experiment on the ESGF data portal.
freq : str, optional
Frequency of the data (default is 'mon').
grid : str, optional
Type of grid (default is 'g*').
version : str, optional
Version of the data being queried (default is 'latest').
variant : str, optional
Label for the variant of the data being queried (default is 'r1i1p1f1').
mipera : str, optional
Name of the CMIP era being queried (default is 'CMIP6').
diresgf : str, optional
Absolute path to the directory where the data is stored (default is '/mnt/reef-ns1002k-esgf/').
verbose : bool, optional
If True, prints the function name at the start and end of execution (default is False).
**kwargs : dict, optional
Other key-value arguments to be passed in the function.
Returns:
--------
List[str]
A list of filepaths corresponding to the specified dataset on the ESGF data portal.
Example:
--------
fp_list = get_esgf_dataset_filepaths('tas', 'CanESM5', 'historical', freq='mon')
Dependencies:
-------------
glob, sys
"""
import glob, sys
if verbose: print('func: get_esgf_dataset_filepaths')
if experimentID in ['1pctCO2', 'piControl', 'historical', 'abrupt-4xCO2']: zwActivity='CMIP'
elif experimentID in ['ssp126', 'ssp245', 'ssp585']: zwActivity='ScenarioMIP'
else: sys.exit('Check experimentID, case not implemented')
if sourceID in ['CESM2', 'CESM2-WACCM']: zwInstitutionID = 'NCAR'
elif sourceID in ['ACCESS-ESM1-5']: zwInstitutionID = 'CSIRO'
elif sourceID in ['CNRM-ESM2-1']: zwInstitutionID = 'CNRM-CERFACS'
elif sourceID in ['CanESM5', 'CanESM5-CanOE']: zwInstitutionID = 'CCCma'
elif sourceID in ['UKESM1-0-LL']: zwInstitutionID = 'MOHC'
elif sourceID in ['GFDL-CM4', 'GFDL-ESM4']: zwInstitutionID = 'NOAA-GFDL'
elif sourceID in ['IPSL-CM6A-LR', 'IPSL-CM6A-LR-INCA']: zwInstitutionID = 'IPSL'
elif sourceID in ['MIROC-ES2L']: zwInstitutionID = 'MIROC'
elif sourceID in ['MPI-ESM1-2-LR', 'ICON-ESM-LR']: zwInstitutionID = 'MPI-M'
elif sourceID in ['NorESM2-LM']: zwInstitutionID = 'NCC'
else: sys.exit('Check sourceID, case not implemented')
ocean_list = ['fgco2', 'intpp', 'o2', 'thetao', 'so', 'agessc', 'po4', 'no3']
if variable in ocean_list: zwTableID = 'O'+freq
elif variable in ['areacello']: zwTableID='Ofx'
elif variable in ['psl']: zwTableID='A'+freq
else: sys.exit('!!! WARNING !!! Check variable, case not implemented')
zwdname = diresgf + mipera +'/'+ zwActivity +'/'+ \
zwInstitutionID +'/'+ sourceID +'/'+ \
experimentID +'/'+ variant +'/'+ zwTableID +'/'+ \
variable+'/'+ grid +'/'+ version +'/'
zwfname = variable +'_'+ zwTableID +'_'+ sourceID +'_'+ \
experimentID +'_'+ variant +'_'+ grid +'*.nc'
if verbose: print('endfunc')
return glob.glob(zwdname + zwfname)
#
Spatial operations¶
In [6]:
def expand_coords(zwda, coord_name):
"""
Expand the specified coordinate dimension to match the shape of zwda.
Args
----
zwda : xarray.DataArray
The DataArray to extract and expand the coordinate from.
coord_name : str
The name of the coordinate to be expanded.
Returns
-------
xarray.DataArray
The expanded coordinate dimension.
Examples
--------
dep_expanded = expand_coords(my_dataarray, 'depth')
"""
import numpy as np
shape = zwda.shape
zwcoord = zwda[coord_name]
common_dims = tuple(set(zwcoord.dims) & set(zwda.dims)) # get the common dimensions between the coordinate and zwda
diff_dims = tuple(set(zwcoord.dims) ^ set(zwda.dims)) # get the different dimensions between the coordinate and zwda
axis_to_expand = zwda.get_axis_num(diff_dims)
len_of_axis_to_expand = np.ones_like(shape)
for aaa in axis_to_expand: len_of_axis_to_expand[aaa]=shape[aaa]
len_of_axis_to_expand = tuple(len_of_axis_to_expand)
zw2 = np.tile(np.expand_dims(zwda[coord_name], axis=axis_to_expand), len_of_axis_to_expand)
output = xr.zeros_like(zwda)
output.values = zw2
output.attrs = zwda[coord_name].attrs
output.name = coord_name
return output
#
def regrid(zwds, method='bilinear', dlon=1, dlat=1, verbose=False):
"""
Regrids the input Dataset to a grid with the specified longitude and latitude intervals using conservative regridding.
Parameters:
-----------
zwds : xr.Dataset
Dataset containing the data to regrid. Note that the Dataset must have the `zwds.cf.get_bounds` attribute.
method : str
Regridding method. Options are
- 'bilinear' (default, recommended)
- 'conservative', **need grid corner information** (recommended)
- 'conservative_normed', **need grid corner information**
- 'patch'
- 'nearest_s2d'
- 'nearest_d2s'
dlon : float (default=1)
Longitude interval in degrees for the output grid.
dlat : float (default=1)
Latitude interval in degrees for the output grid.
Returns:
--------
xr.Dataset
Regridded Dataset.
Example:
--------
# Regrid a Dataset to a 1x1 degree grid
regridded_data = regrid(zwds, dlon=1, dlat=1)
Dependencies:
-------------
xesmf
"""
if verbose: print('func: regrid')
import xesmf as xe
ds_in = zwds.rename({'longitude':'lon', 'latitude':'lat', 'j':'y', 'i':'x'})
zlat = ds_in.lat.values
zlon = ds_in.lon.values
zlatb, zlonb = compute_horizontal_bounds(zlon=zlon, zlat=zlat)
zlatbda = xr.DataArray(data=zlatb,
name='lat_b',
dims=['y_b', 'x_b'],
coords=dict(
lon_b=(["y_b", "x_b"], zlonb),
lat_b=(["y_b", "x_b"], zlatb)
),
attrs=dict(description="grid latitudnal bounds",units="degN")
)
zlonbda = xr.DataArray(data=zlonb,
name='lon_b',
dims=['y_b', 'x_b'],
coords=dict(
lon_b=(["y_b", "x_b"], zlonb),
lat_b=(["y_b", "x_b"], zlatb)
),
attrs=dict(description="grid longitudinal bounds",units="degN")
)
ds_in = ds_in.assign_coords({'lon_b':zlonbda, 'lat_b':zlatbda})
ds_out = xe.util.grid_global(dlon, dlat) # Here I define the output grid, in that case I want a regular 1 by 1 degree
regridder = xe.Regridder(ds_in, ds_out, method, ignore_degenerate=True, unmapped_to_nan=True) # this the line if I do not have the weight alread
zwds_regrided = regridder(ds_in).rename({'lon':'longitude', 'lat':'latitude', 'x':'i', 'y':'j'})
ds_in = ds_in.rename({'lon':'longitude', 'lat':'latitude', 'x':'i', 'y':'j'})
for kkk in zwds_regrided.variables.keys(): zwds_regrided.variables[kkk].attrs = ds_in.variables[kkk].attrs
return zwds_regrided
#
def compute_horizontal_bounds(zlon=None, zlat=None, verbose=False):
"""
Returns the bounds of the grid from 2 numpy arrays giving the latitude and longitude
of the point in the middle of the grid-cells.
Parameters:
-----------
zlon : np.ndarray
Array of longitudes.
zlat : np.ndarray
Array of latitudes.
Returns:
--------
Tuple of two numpy arrays: latitude bounds and longitude bounds.
Example:
--------
# Compute horizontal bounds for a grid
lat_b, lon_b = compute_horizontal_bounds(zlon, zlat)
Dependecies:
------------
numpy
"""
import numpy as np
if verbose: print('func: compute_horizontal_bounds')
if (zlon.any()==None) | (zlat.any()==None):
exit('WARNING! no zlon or no zlat')
#
zlatb = 0.5*( np.roll(zlat, 1, axis=0) + zlat)
zlatb[ 0, :] = zlat[0, :] - (zlatb[1, :] - zlat[0, :])
zlatb1 = zlat[-1, :] + (zlat[-1, :] - zlatb[-1, :])
zlatb = np.concatenate([zlatb, zlatb1[np.newaxis, :]], axis=0)
zlatb = np.concatenate([zlatb, zlatb[:, -1][:, np.newaxis]], axis=1)
zlonb = 0.5*( np.roll(zlon, 1, axis=1) + zlon)
zlonb[:, 0] = zlon[:, 0] - (zlonb[:, 1] - zlon[:, 0])
zlonb1 = zlon[:, -1] + (zlon[:, -1] - zlonb[:, -1])
zlonb = np.concatenate([zlonb, zlonb1[:, np.newaxis]], axis=1)
zlonb = np.concatenate([zlonb, zlonb[-1, :][np.newaxis, :]], axis=0)
return zlatb, zlonb
#
def vertical_interpolation(zwda, newdep, dimensions='zyx', verbose=False):
from scipy.interpolate import interp1d
if verbose: print('func: vertical_interpolation')
depaxis = zwda.get_axis_num('k')
zwdep = zwda.depth
# fff = interp1d(zwdep, zwda, axis=0, assume_sorted=True, fill_value='extrapolate')
fff = interp1d(zwdep, zwda, axis=depaxis, assume_sorted=True, bounds_error=False, fill_value=float('nan'))
# fff = interp1d(zwdep, zwda, axis=1, assume_sorted=True, fill_value='extrapolate')
aaa = fff(newdep)
if dimensions=='zyx':
output = xr.DataArray(data=aaa,
coords={'depth':newdep,
'latitude':zwda.latitude,
'longitude':zwda.longitude},
dims=('k', 'j', 'i'),
attrs=zwda.attrs,
name=zwda.name)
elif dimensions=='zy':
output = xr.DataArray(data=aaa,
coords={'latitude':zwda.latitude,
'depth':newdep},
dims=('k', 'j'),
attrs=zwda.attrs,
name=zwda.name)
#
return output
#
def compute_vertical_average(zwda, zmin=None, zmax=None, verbose=False):
"""
Computes the vertical average of a 3D or 4D xarray DataArray over a specified depth range.
Parameters:
-----------
zwda : xr.DataArray
3D or 4D DataArray to average.
zmin : float
Minimum depth in meters to average over. (Default is None)
zmax : float
Maximum depth in meters to average over. (Default is None)
verbose : bool
Print verbose output if True.
Returns:
--------
xr.DataArray
2D or 3D DataArray containing the vertical average of the input DataArray over the specified depth range.
Example:
--------
avg_data = compute_vertical_average(data, zmin=0, zmax=100)
Dependencies:
-------------
scipy.interpolate.interp1d
"""
if verbose: print('func: compute_vertical_average')
from scipy.interpolate import interp1d
depaxis = zwda.get_axis_num('k')
zwdep = zwda.depth
zwdepbnds = 0.5 * (zwdep + zwdep.shift(k=1, fill_value=0))
zwdepbnds[0] = 0
zwDdep = zwdepbnds.shift(k=-1) - zwdepbnds
zwDdep[-1] = 2 * (zwdep[-1] - zwdepbnds[-1])
omask = ~(zwda.isnull())
bathy = (omask * zwDdep).sum(dim='k')
omaskH = ~(zwda.isnull().all(dim='k'))
if (zmin is None) & (zmax is None):
zw2 = (zwda*zwDdep).sum(axis=depaxis)
output = xr.where(omaskH, zw2/bathy, np.nan)
elif (not zmin is None) & (zmax is None):
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
zw2 = (zwda*zwDdep).sum(axis=depaxis) - fff(zmin)
zmin3D = zmin+bathy*0
waterthick = xr.where(zmin3D <= bathy, bathy - zmin3D, np.nan)
output = xr.where(omaskH, zw2/waterthick, np.nan)
elif (zmin is None) & (not zmax is None):
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
zw2 = fff(zmax)
zmax3D = zmax+bathy*0
waterthick = xr.where(zmax3D <= bathy, zmax3D, bathy)
output = xr.where(omaskH, zw2/waterthick, np.nan)
else:
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
zw2 = fff(zmax) - fff(zmin)
zmax3D = zmax+bathy*0
zmin3D = zmin+bathy*0
waterthick = np.nan + bathy*0
waterthick = xr.where((zmin3D<=bathy) & (zmax3D<=bathy), zmax3D - zmin3D, waterthick)
waterthick = xr.where((zmin3D<=bathy) & (zmax3D>bathy) , bathy - zmin3D, waterthick)
output = xr.where(omaskH, zw2/waterthick, np.nan)
#
output.attrs=zwda.attrs
return output
#
def compute_vertical_sum(zwda, zmin=None, zmax=None, verbose=False):
"""
Computes the vertical average of a 3D or 4D xarray DataArray over a specified depth range.
Parameters:
-----------
zwda : xr.DataArray
3D or 4D DataArray to average.
zmin : float
Minimum depth in meters to sum over. (Default is None)
zmax : float
Maximum depth in meters to sum over. (Default is None)
verbose : bool
Print verbose output if True.
Returns:
--------
xr.DataArray
2D or 3D DataArray containing the vertical average of the input DataArray over the specified depth range.
Example:
--------
avg_data = compute_vertical_average(data, zmin=0, zmax=100)
Dependencies:
-------------
scipy.interpolate.interp1d
"""
if verbose: print('func: compute_vertical_average')
from scipy.interpolate import interp1d
depaxis = zwda.get_axis_num('k')
zwdep = zwda.depth
zwdepbnds = 0.5*(zwdep + zwdep.shift(k=1, fill_value=0))
zwdepbnds[0] = 0
zwDdep = zwdepbnds.shift(k=-1) - zwdepbnds
zwDdep[-1] = 2*(zwdep[-1] - zwdepbnds[-1])
if 'year' in zwda.dims: mask = zwda.isel(year=0, drop=True).isnull().all(dim='k')
else: mask = zwda.isnull().all(dim='k')
if (zmin is None) & (zmax is None):
output = xr.where(mask==1, np.nan, (zwda*zwDdep).sum(axis=depaxis))
elif (not zmin is None) & (zmax is None):
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
output = xr.DataArray((zwda*zwDdep).sum(axis=depaxis) - fff(zmin),
coords=zwda.isel(k=0, drop=True).coords,
dims=zwda.isel(k=0, drop=True).dims,
attrs=zwda.isel(k=0, drop=True).attrs)
output = output.where(mask==0)
elif (zmin is None) & (not zmax is None):
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
output = xr.DataArray(fff(zmax),
coords=zwda.isel(k=0, drop=True).coords,
dims=zwda.isel(k=0, drop=True).dims,
attrs=zwda.isel(k=0, drop=True).attrs)
output = output.where(mask==0)
else:
zw = (zwda*zwDdep).cumsum(axis=depaxis)
fff = interp1d(zwDdep.cumsum(), zw, axis=depaxis, bounds_error=False)
output = xr.DataArray(fff(zmax) - fff(zmin),
coords=zwda.isel(k=0, drop=True).coords,
dims=zwda.isel(k=0, drop=True).dims,
attrs=zwda.isel(k=0, drop=True).attrs)
output = output.where(mask==0)
#
output.attrs=zwda.attrs
return output
#
def zonal_avg(zwda, weights, longitudes=None, return_3Dselection=False, verbose=False):
"""
Compute weithed zonal average between lonmin and lonmax
Inputs:
- zwda: xarray DataArray
- weights: xarray DataArray used for weighting zonal average
- lonmin, lonmax: floats defining longitude for avereging
- return_3Dselection: boolean to return or not the data selected for averaging. Default is False
- verbose: boolean controlling prints in the code
Ouput:
xarray DataArray with zonal average.
If return_3Dselection=True, then data selected for zonal averagin are also returned
"""
from scipy.interpolate import interp1d
if verbose: print('func: zonal_avg')
if 'k' in weights.dims: weight_for_zlat = weights.isel(k=0, drop=True)
else: weight_for_zlat = weights
if longitudes==None:
# compute zonal average weighted
with xr.set_options(keep_attrs=True):
output = zwda.weighted(weights.fillna(0)).mean(dim='i', skipna=True)
zwlat = zwda['latitude'].weighted(weight_for_zlat.fillna(0)).mean(dim='i', skipna=True)
#
else:
# Select longitudes
LONSEL = (zwda.longitude<=longitudes[1]) & (zwda.longitude>=longitudes[0])
zw = (zwda.T).where(LONSEL.T, np.nan).T
zw2 = (zwda['latitude'].T).where(LONSEL.T, np.nan).T
# compute zonal average weighted
with xr.set_options(keep_attrs=True):
output = zw.weighted(weights.fillna(0)).mean(dim='i', skipna=True)
zwlat = zw2.weighted(weight_for_zlat.fillna(0)).mean(dim='i', skipna=True)
#
#
#______________
# Adjust latitude zonally average
if zwlat.isnull().any(): # if there is nan values in zwlat, replace by the mean non weighted
if longitudes==None:
zwlat = xr.where(zwlat.isnull(),
zwda['latitude'].mean(dim='i', skipna=True),
zwlat)
else:
zwlat = xr.where(zwlat.isnull(),
xr.where( LONSEL.T, zwda['latitude'].T, np.nan).T.mean(dim='i', skipna=True),
zwlat)
#
#
if zwlat.isnull().any(): # if there is nan values in zwlat_iavg, replace by extrapolation
aaa = zwlat.values
t1 = np.arange(len(aaa))
bbb = aaa[~np.isnan(aaa)]
t2 = t1[~np.isnan(aaa)]
f = interp1d(t2, bbb, fill_value='extrapolate')
zwlat[np.isnan(aaa)] = f(t1[np.isnan(aaa)])
#
#______________
# Set new latitude coordinates
with xr.set_options(keep_attrs=True): output = output.assign_coords({'latitude':zwlat})
if (return_3Dselection) & (longitudes!=None):
return output, zw
else: return output
#
def gridArea(zlon=None, zlat=None, rd=6371000.00, verbose=False):
"""
Compute grid_area from longitude and latitude
Inputs:
- zlon: 2D numpy Array with longitude. If None (default) zlon
and zlat are 1deg grid
- zlat: same as zlon but with latitude
- rd: float with Earth radius: 6371000.00m (default)
Output: xarray DataArray with grid cell area
"""
# adapted from Jean Negrel:
# for n in range(len(lat)):
# for m in range(len(lon)):
# rlat1 = np.deg2rad(lat[n]+0.5)
# rlat2 = np.deg2rad(lat[n]-0.5)
# rlon1 = np.deg2rad(lon[m]+0.5)
# rlon2 = (np.deg2rad(lon[m]-0.5))
# drlon = rlon1 - rlon2
# if abs(drlon) >= (2*np.pi - abs(drlon)) :
# drlon = drlon+2*np.pi
# grid[n,m] = drlon*abs( np.sin(rlat2) - np.sin(rlat1) )
# #print(grid.sum()-4*np.pi) # Must be as close as possible to 0
# grid = grid*rd*rd
# grid = xr.DataArray(grid, coords=[lat, lon], dims=['lat', 'lon'])
# #print(grid.sum()) # Actual Earth area: 510,065,623 km2 (source: Wikipedia)
if verbose: print('func: gridArea')
if (zlon.any()==None) | (zlat.any()==None):
zlon, zlat = np.meshgrid(np.arange(-179.5, 180, 1), np.arange(-89.5, 90, 1))
#
zlat1 = 0.5*( np.roll(zlat, -1, axis=0) + zlat)
zlat1[-1, :] = zlat[-1, :] + (zlat[-1, :] - zlat1[-2, :])
zlat2 = 0.5*( np.roll(zlat, 1, axis=0) + zlat)
zlat2[ 0, :] = zlat[0, :] - (zlat2[1, :] - zlat[0, :])
zlon1 = 0.5*( np.roll(zlon, -1, axis=1) + zlon)
zlon1[:, -1] = zlon[:, -1] + (zlon[:, -1] - zlon1[:, -2])
zlon2 = 0.5*( np.roll(zlon, 1, axis=1) + zlon)
zlon2[:, 0] = zlon[:, 0] - (zlon2[:, 1] - zlon[:, 0])
zlat1_rad = np.deg2rad(zlat1)
zlat2_rad = np.deg2rad(zlat2)
zlon1_rad = np.deg2rad(zlon1)
zlon2_rad = np.deg2rad(zlon2)
dlon_rad = zlon1_rad - zlon2_rad
dlon_rad = np.where( (abs(dlon_rad) >= (2*np.pi - abs(dlon_rad))), \
dlon_rad+2*np.pi, dlon_rad)
grid_area = rd*rd*dlon_rad*abs( np.sin(zlat2_rad) - np.sin(zlat1_rad) )
output = xr.DataArray(data=grid_area,
name='area',
dims=['j', 'i'],
coords=dict(
longitude=(["j", "i"], zlon),
latitude=(["j", "i"], zlat)
),
attrs=dict(description="Grid area",units="m2")
)
if verbose: print(f" Total grid area is {output.sum().values*1e-6:.1f}km2\
\n i.e. {output.sum()*1e-6/510065623*100:.1f}% of the actual Earth area (510065623km2 source: Wikipedia)\
\n Total grid area is {output.sum()*1e-6 - 510065623:.1f}km2 of the actual Earth area")
# if verbose: print("Total grid area is %.1f %% of actual Earth area: \
# 510,065,623 km2 (source: Wikipedia)" %output.sum()*1e-6/510065623*100)
return output
#
def compute_vertical_bounds_thickness(zwdep1D):
"""
Compute vertical bounds and thicknesses 1D depth array.
This function takes a one-dimensional series of depth values and computes
the vertical boundaries and thicknesses for each layer.
Parameters
----------
zwdep1D : array-like
One-dimensional array or series of depth values.
Returns
-------
zwdepbnds1D : array-like
One-dimensional array representing the vertical bounds for each layer.
zwDdep1D : array-like
One-dimensional array representing the thicknesses of each layer.
Notes
-----
- The input is assumed to have a `shift` method, similar to a pandas Series.
- The first vertical boundary is set explicitly to zero (surface level).
- The thickness of the last layer is adjusted to ensure accurate representation.
Example
-------
>>> import pandas as pd
>>> depths = pd.Series([10, 20, 30, 40])
>>> bounds, thicknesses = compute_vertical_bounds_(depths)
>>> print(bounds)
0 0.0
1 5.0
2 15.0
3 25.0
dtype: float64
>>> print(thicknesses)
0 5.0
1 10.0
2 10.0
3 10.0
dtype: float64
"""
zwdepbnds1D = 0.5 * (zwdep1D + zwdep1D.shift(k=1, fill_value=0))
zwdepbnds1D[0] = 0
zwDdep1D = zwdepbnds1D.shift(k=-1) - zwdepbnds1D
zwDdep1D[-1] = 2 * (zwdep1D[-1] - zwdepbnds1D[-1])
return zwdepbnds1D, zwDdep1D
#
Mapping functions¶
In [7]:
def adjust_vmin_vmax(data, q=0.1, round_up=True):
"""
Computes vmin and vmax based on quantiles of the data and optionally
rounds them up to values that make sense.
Args:
- data (ndarray): 2D array of data
- q (float, optional): quantile used to compute vmin and vmax (default: 0.1)
- round_up (bool, optional): whether to round up vmin and vmax to "nice" values (default: True)
Returns:
- tuple: vmin and vmax values
"""
vmin = np.nanpercentile(data, q * 100)
vmax = np.nanpercentile(data, (1 - q) * 100)
if round_up:
mag = 10 ** np.floor(np.log10(vmax - vmin))
vmin1 = np.floor(vmin / mag)
vmax1 = np.ceil(vmax / mag)
nb_of_mag= np.round((vmax-vmin)/mag)
icount = 0
while (vmax1-vmin1)/nb_of_mag > 1.2:
dvmin = vmin-vmin1*mag
dvmax = vmax1*mag - vmax
if dvmax>dvmin: vmax1=np.round(vmax1-.1, 1)
else: vmin1=np.round(vmin1+.1, 1)
#
vmax = vmax1*mag
vmin = vmin1*mag
#
return vmin, vmax
#
def map_whole_globe(zwdataarray, zwax=None, bcolorbar=True, zwcbLabel='', zwcbExtend='both', verbose=False, **pcmkwargs):
"""
Plot a global map of the input DataArray
Inputs:
- zwax are GeoAxes created with cartopy
e.g.:
lonlatproj = ccrs.PlateCarree()
ccrsproj = ccrs.Robinson(central_longitude=-155)
fig = plt.figure(figsize = (10, 10))
ax = plt.axes(projection=ccrsproj)
pcmkwargs = dict(transform=lonlatproj)
map_whole_globe(zwda, ax, **pcmkwargs)
if None (default), theyr are created within the function
- zwcbLabel: string to set label of colorbar. Default is ''
- zwcbExtend: 'none', 'min', 'max' or 'both' (default)
- **pcmkwargs are kwargs pass to matplotlib pcolormesh function
Outputs: zwax the axes and zwpcm the pcolormesh
"""
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
import cartopy.crs as ccrs
if verbose: print('func: map_whole_globe')
zwlonlatproj = ccrs.PlateCarree()
try:
pcmkwargs
except NameError:
pcmkwargs_exists = False
else:
pcmkwargs_exists = True
#
if pcmkwargs_exists:
original_pcmkwargs = pcmkwargs.copy()
if not 'vmin' in pcmkwargs: pcmkwargs['vmin'] = zwdataarray.quantile(.1).values
elif pcmkwargs['vmin'] is None: pcmkwargs['vmin'] = zwdataarray.quantile(.1).values
if not 'vmax' in pcmkwargs: pcmkwargs['vmax'] = zwdataarray.quantile(.9).values
elif pcmkwargs['vmax'] is None: pcmkwargs['vmax'] = zwdataarray.quantile(.9).values
if not 'transform' in pcmkwargs: pcmkwargs['transform'] = zwlonlatproj
if not 'cmap' in pcmkwargs:
if (pcmkwargs['vmin']*pcmkwargs['vmax']<0): a = cm.get_cmap('RdBu',64)
else: a = cm.get_cmap('viridis',64)
if zwcbExtend=='both':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
elif zwcbExtend=='max':
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_over(colors[-1])
newcmap.set_under("none")
elif zwcbExtend=='min':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:])
newcmap.set_over("none")
newcmap.set_under(colors[0])
else:
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors)
newcmap.set_over("none")
newcmap.set_under(colors[0])
#
newcmap.set_bad("none")
pcmkwargs['cmap'] = newcmap
#
else:
pcmkwargs['vmin'] = zwdataarray.quantile(.1).values
pcmkwargs['vmax'] = zwdataarray.quantile(.9).values
pcmkwargs={'transform':zwlonlatproj}
if (pcmkwargs['vmin']*pcmkwargs['vmax']<0): a = cm.get_cmap('RdBu',64)
else: a = cm.get_cmap('viridis',64)
if zwcbExtend=='both':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
elif zwcbExtend=='max':
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_over(colors[-1])
newcmap.set_under("none")
elif zwcbExtend=='min':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:])
newcmap.set_over("none")
newcmap.set_under(colors[0])
else:
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors)
newcmap.set_over("none")
newcmap.set_under(colors[0])
#
newcmap.set_bad("none")
pcmkwargs['cmap'] = newcmap
#
XXX = zwdataarray['longitude'].values
YYY = zwdataarray['latitude'].values
ZZZ = zwdataarray.values
if not zwax is None:
zwpcm = zwax.pcolormesh(XXX, YYY, ZZZ, **pcmkwargs)
else:
print("WARNING! zwax is None, create zwfig and zwax")
zwccrsproj = ccrs.Robinson(central_longitude=-155)
zwfig = plt.figure(figsize = (10, 10))
zwax = plt.axes(projection=zwccrsproj)
zwpcm = zwax.pcolormesh(XXX, YYY, ZZZ, **pcmkwargs)
#
zwax.set_global()
add_gridlines_coastline_and_land(zwax)
# zwax.add_feature(cfeature.NaturalEarthFeature('physical', 'land', '110m',edgecolor='none',facecolor='wheat'))
# zwax.add_feature(cfeature.COASTLINE)
# zwax.gridlines(draw_labels=True, x_inline=False, y_inline=False,
# color='k', linestyle='dashed', linewidth=0.5)
if bcolorbar:
zwcb = plt.colorbar(zwpcm, label=zwcbLabel, extend=zwcbExtend)
zw = zwax.get_position()
nx0 = zw.x1+.07*zw.width
ny0 = zw.y0+.1*zw.height
nhh = .8*zw.height
nww = .1*nhh
zwcb.ax.set_position([nx0, ny0, nww, nhh])
ttt=np.linspace(zwcb.vmin, zwcb.vmax, int(len(zwcb.cmap.colors)/2)+1)
zwcb.ax.set_yticks(ttt)
#___________
# # restore the original kwargs before returning
if pcmkwargs_exists:
pcmkwargs.clear()
pcmkwargs.update(original_pcmkwargs)
#
return zwax, zwpcm
#
def add_gridlines_coastline_and_land(zax, zxlocs=[-120, -60, 0, 60, 120, 180], \
zylocs=[-60, -40, -20, 0, 20, 40, 60], \
noticklabels=False, verbose=False):
import cartopy.feature as cfeature
import matplotlib.ticker as mticker
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER, LatitudeFormatter, LongitudeFormatter
if verbose: print('func: add_gridlines_coastline_and_land')
# zax.add_feature(cfeature.NaturalEarthFeature('physical', 'land', '110m',edgecolor='none',facecolor='wheat'))
zax.add_feature(cfeature.LAND, facecolor='wheat')
zax.add_feature(cfeature.COASTLINE, linewidth=.5)
if not noticklabels: gl = zax.gridlines(draw_labels=['left', 'right'],
x_inline=False, y_inline=False,
xlocs = mticker.FixedLocator(zxlocs),
xlabel_style = {'size': 'small'},
ylocs = mticker.FixedLocator(zylocs),
ylabel_style = {'size': 'small'}, dms=True,
color='k', linestyle='dashed', linewidth=0.5, alpha=.5)
else: gl = zax.gridlines(draw_labels=False,
x_inline=False, y_inline=False,
xlocs = mticker.FixedLocator(zxlocs),
ylocs = mticker.FixedLocator(zylocs),
dms=True,
color='k', linestyle='dashed', linewidth=0.5, alpha=.5)
# gl = zwax.gridlines(draw_labels=True, x_inline=False, y_inline=False,
# color='k', linestyle='dashed', linewidth=0.5)
return gl
#
def map_orthographic(zwdataarray, zoombox=(0, 0), \
zwax=None, zwcbLabel='', zwcbExtend='both', **pcmkwargs):
"""
Plot the input DataArray
zoombox either indicate the center [clon, clat] of the Orthographic projection (list of 2)
or the bounds of the projection [lonmin, lonmax, latmin, latmax]. Default is (0, 0)
zwax are GeoAxes created with cartopy
e.g.:
lonlatproj = ccrs.PlateCarree()
ccrsproj = ccrs.Orthographic(central_longitude=-20, central_latitude=-25)
fig = plt.figure(figsize = (10, 10))
ax = plt.axes(projection=ccrsproj)
pcmkwargs = dict(transform=lonlatproj)
map_whole_globe(zwda, ax, **pcmkwargs)
if zwax = None (default), they are created within the function
"""
print('func: map_orthographic')
try:
pcmkwargs
except NameError:
pcmkwargs_exists = False
else:
pcmkwargs_exists = True
#
zwlonlatproj = ccrs.PlateCarree()
if pcmkwargs_exists:
if not 'transform' in pcmkwargs: pcmkwargs['transform'] = zwlonlatproj
else: pcmkwargs={'transform':zwlonlatproj}
XXX = zwdataarray['longitude'].values
YYY = zwdataarray['latitude'].values
ZZZ = zwdataarray.values
if not zwax is None:
print("WARNING! zwax is given, ignoring zoombox")
zwpcm = zwax.pcolormesh(XXX, YYY, ZZZ, **pcmkwargs)
zwax.set_global()
zxlocs = list(np.arange(-150, 200, 30))
add_gridlines_coastline_and_land(zwax, zxlocs=zxlocs)
else:
print("WARNING! zwax is None, create zwfig and zwax")
if len(zoombox)==4:
lonmin, lonmax, latmin, latmax = zoombox
clon, clat = lonmin+.5*(lonmax-lonmin), latmin+.5*(latmax-latmin)
elif len(zoombox)==2: clon, clat= zoombox
else: sys.exit('ERROR! Check zoombox, it should be a list of length 2 or 4')
zwccrsproj = ccrs.Orthographic(central_longitude=clon, central_latitude=clat)
zwfig = plt.figure(figsize = (5, 5))
zwax = plt.axes(projection=zwccrsproj)
zwpcm = zwax.pcolormesh(XXX, YYY, ZZZ, **pcmkwargs)
if len(zoombox)==4 :
xmin, _ = zwax.projection.transform_point(lonmin, clat, zwlonlatproj)
xmax, _ = zwax.projection.transform_point(lonmax, clat, zwlonlatproj)
_, ymin = zwax.projection.transform_point(clon, latmin, zwlonlatproj)
_, ymax = zwax.projection.transform_point(clon, latmax, zwlonlatproj)
zwax.set_extent([xmin,xmax,ymin,ymax], crs=zwax.projection)
zxlocs = list(np.arange(lonmin, lonmax, np.round((50+60)/4, -1)))
add_gridlines_coastline_and_land(zwax, zxlocs=zxlocs)
elif len(zoombox)==2:
zwax.set_global()
zxlocs = list(np.arange(-150, 200, 30))
add_gridlines_coastline_and_land(zwax, zxlocs=zxlocs)
#
#
zwcb = plt.colorbar(zwpcm, label=zwcbLabel, extend=zwcbExtend)
zw = zwax.get_position()
nx0 = zw.x1+.15*zw.width
ny0 = zw.y0+.1*zw.height
nhh = .8*zw.height
nww = .1*nhh
zwcb.ax.set_position([nx0, ny0, nww, nhh])
ttt=np.linspace(zwcb.vmin, zwcb.vmax, int(len(zwcb.cmap.colors)/2)+1)
zwcb.ax.set_yticks(ttt)
return zwax, zwpcm
#
def plot_section(dataarray, ax=None, bcolorbar=True, cbLabel='', cbExtend='both', title='Zonal average', plottype='pcolormesh',
xlim=(-90, 90), xlabel='Deg. North', ylim=(5000, -10), ylabel='Depth [m]', inline=False,
kwpcm={}, kwct={}, verbose=False):
"""
Plot a vertical & meridional section
Inputs:
- dataarray: 2D xarray DataArray to plot
- ax: a set of matplolib axes (e.g. plt.axes), if None (default) axes are created
- cbLabel: a string for labelling the colorbar. Default is ''
- cbExtend: 'none', 'min', 'max' or 'both' (default)
- title: string to give title to the plot. Default is 'Zonal average'
- plottype: 'pcolormesh' (default) or 'contour' or 'both'. To define the type of plot.
- xlim: tuple defining the x axis limits. Default is (-90, 90)
- ylim: tuple defining the y axis limits. Default is (1000, -10)
- xlabel: string for x axis label
- ylabel: string for y axis label
- inline: boolean to set contour labels. Default is False
- kwpcm: kwargs to pass to pcolormesh
- kwct: kwargs to pass to contour
"""
#___________
# Import
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap
if verbose: print('func: plot_section')
#___________
# Do kwpcm exist?
try:
kwpcm
except NameError:
kwpcm_exists = False
else:
kwpcm_exists = True
#
#___________
# set cmap, vmin and vmax if they are not define
if kwpcm_exists:
original_kwpcm = kwpcm.copy()
if not 'cmap' in kwpcm:
a = cm.get_cmap('viridis',64)
if cbExtend=='both':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
elif cbExtend=='max':
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_over(colors[-1])
newcmap.set_under("none")
elif cbExtend=='min':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:])
newcmap.set_over("none")
newcmap.set_under(colors[0])
else:
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors)
newcmap.set_over("none")
newcmap.set_under(colors[0])
#
newcmap.set_bad("none")
kwpcm['cmap'] = newcmap
#
if not 'vmin' in kwpcm: kwpcm['vmin'] = dataarray.quantile(.1).values
if not 'vmax' in kwpcm: kwpcm['vmax'] = dataarray.quantile(.9).values
elif (not kwpcm_exist) & (plottype in ['pcolormesh', 'both']):
kwpcm = {}
a = cm.get_cmap('viridis',64)
if cbExtend=='both':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
elif cbExtend=='max':
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_over(colors[-1])
newcmap.set_under("none")
elif cbExtend=='min':
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:])
newcmap.set_over("none")
newcmap.set_under(colors[0])
else:
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors)
newcmap.set_over("none")
newcmap.set_under(colors[0])
#
newcmap.set_bad("none")
kwpcm['cmap'] = newcmap
kwpcm['vmin'] = dataarray.quantile(.1).values
kwpcm['vmax'] = dataarray.quantile(.9).values
#
#___________
# Do kwct exist?
try:
kwct
except NameError:
kwct_exists = False
else:
kwct_exists = True
#
#___________
# set colors for contour
if kwct_exists:
original_kwct = kwct.copy()
if not 'colors' in kwct: kwct['colors'] = 'red'
elif (not kwct_exists) & (plottype in ['contour', 'both']):
kwct = {'colors':'red'}
#
#___________
# Create axes if needed
if ax is None:
if verbose: print("WARNING! ax is None, create ax")
ax = plt.axes()
#
#___________
# Plot
if plottype in ['pcolormesh', 'both']: zwpcm = ax.pcolormesh(dataarray.latitude, dataarray.depth, dataarray, **kwpcm)
if plottype in ['contour', 'both']:
zwct = ax.contour(dataarray.latitude, dataarray.depth, dataarray, **kwct)
ax.clabel(zwct, inline=inline)
#
#___________
# Decorate plot
ax.set_ylim(ylim)
ax.set_ylabel(ylabel)
ax.set_xlim(xlim)
ax.set_xlabel(xlabel)
ax.set_title(title)
#___________
# Colorbar
if bcolorbar:
try:
zwcb = plt.colorbar(zwpcm, label=cbLabel, extend=cbExtend)
zw = ax.get_position()
nx0 = zw.x1+.07*zw.width
ny0 = zw.y0+.1*zw.height
nhh = .8*zw.height
nww = .1*nhh
zwcb.ax.set_position([nx0, ny0, nww, nhh])
ttt=np.linspace(zwcb.vmin, zwcb.vmax, int(len(zwcb.cmap.colors)/2)+1)
zwcb.ax.set_yticks(ttt)
except:
if verbose: print('No colorbar')
#
#
#___________
# # restore the original kwargs before returning
if kwpcm_exists:
kwpcm.clear()
kwpcm.update(original_kwpcm)
#
if kwct_exists:
kwct.clear()
kwct.update(original_kwct)
#
#___________
# Return
if ('zwpcm' in locals()) & ('zwct' in locals()):
return zwpcm, zwct
elif ('zwpcm' in locals()):
return zwpcm
elif ('zwct' in locals()):
return zwct
else:
print('Nothing have been plot')
return
#
Diagnostics¶
In [8]:
def compute_potential_density_anomaly( temp, sali ):
"""
Computes the potential density anomaly with reference pressure of 0 dbar given temperature and salinity arrays.
Parameters:
-----------
temp : xarray.DataArray
Array of temperature values.
Should have the following dimensions: latitude, longitude, and depth.
sali : xarray.DataArray
Array of salinity values.
Should have the following dimensions: latitude, longitude, and depth.
Returns:
--------
xarray.DataArray
Array of potential density anomaly values with reference pressure of 0 dbar.
Has the same dimensions as `temp` and `sali`.
Dependencies:
-------------
gsw_xarray
"""
import gsw_xarray as gsw
zwlat = temp['latitude']
zwlon = temp['longitude']
zwlev = temp['depth']
# Compute sea pressure
zwseapr = gsw.conversions.p_from_z(-zwlev, zwlat)
# Compute absolute salinity
zwSA = gsw.conversions.SA_from_SP(sali, zwseapr, zwlon, zwlat)
# Compute conservative temperature
zwCT = gsw.conversions.CT_from_pt(zwSA, temp)
# Compute potential density anomaly
zwsigma0 = gsw.density.sigma0(zwSA, zwCT)
zwsigma0.attrs['comment']= 'Potential density anomaly with reference pressure of 0 dbar, this being this particular \
potential density minus 1000 kg/m^3. This function has inputs of Absolute Salinity and Conservative Temperature. \
This function uses the computationally-efficient expression for specific volume in terms of SA, CT and p (Roquet et al., 2015).'
zwsigma0.attrs['long_name']='Potential density anomaly with reference pressure of 0 dbar'
addtxt = str(datetime.datetime.now())+' '+'gsw.density.sigma0 to compute potential density anomaly with reference pressure of 0 dbar'
try : zwsigma0.attrs['history'] = addtxt+' ; '+zwsigma0.attrs['history']
except: zwsigma0.attrs['history']=addtxt
return zwsigma0
#
def coloring_2D_bins(xxx, yyy, ccc, stat='mean', nbin=100, delta_bin=None):
"""
Compute the binned statistical summary of coloring values for 2D data arrays.
This function flattens the 2D input arrays, removes nan values, and computes the
specified statistical summary (by default, the mean) for the given bins in the
two-dimensional space defined by `xxx` and `yyy`.
Parameters
----------
xxx : array-like
2D array representing the x-coordinates of the data points.
yyy : array-like
2D array representing the y-coordinates of the data points.
ccc : array-like
2D array representing the coloring values corresponding to the data points.
stat : str, optional
The statistic to compute (default is 'mean'). Possible values are 'mean',
'median', 'count', 'sum', 'std', etc., as supported by scipy.stats.binned_statistic_dd.
nbin : int, optional
Number of bins along each dimension (default is 100) if `delta_bin` is not provided.
delta_bin : array-like, optional
Array of length 2 specifying the bin width for each dimension. If provided,
`nbin` is ignored, and the bins are computed based on the specified widths.
Returns
-------
dict
A dictionary with the following keys:
'xxx': array
The edges of the bins along the x-dimension.
'yyy': array
The edges of the bins along the y-dimension.
'ccc': array
The computed statistical summary for each bin, after transposing the result.
Examples
--------
>>> import numpy as np
>>> from scipy.stats import binned_statistic_dd
>>> xxx = np.random.random((100, 100))
>>> yyy = np.random.random((100, 100))
>>> ccc = np.random.random((100, 100))
>>> result = coloring_2D_bins(xxx, yyy, ccc, stat='mean', nbin=50)
>>> print(result['xxx'])
>>> print(result['yyy'])
>>> print(result['ccc'])
"""
# Flatten the array to 1D
xxx_flat = xxx.flatten()
yyy_flat = yyy.flatten()
ccc_flat = ccc.flatten()
# Remove nans
nans_xxx, x = nan_helper(xxx_flat)
nans_yyy, x = nan_helper(yyy_flat)
nans_ccc, x = nan_helper(ccc_flat)
no_nans = (~nans_xxx) & (~nans_yyy) & (~nans_ccc)
zwxxx = xxx_flat[no_nans]
zwyyy = yyy_flat[no_nans]
zwccc = ccc_flat[no_nans]
# Combine xxx_flat and yyy_flat for binning
sample = np.vstack((zwxxx, zwyyy)).T
# Compute the average of coloring for each bin
if (delta_bin==None):
num_bins = [nbin, nbin] # Number of bins for each dimension, adjust as required
stat_result, edges, bin_number = stats.binned_statistic_dd(sample, values=zwccc, statistic=stat, bins=num_bins)
else:
sample_min = np.min(sample, axis=0)
sample_max = np.max(sample, axis=0)
num_bins = [np.arange(sample_min[dim], sample_max[dim]+.5*delta_bin[dim], delta_bin[dim]) for dim in range(sample.shape[1])]
stat_result, edges, bin_number = stats.binned_statistic_dd(sample, values=zwccc, statistic=stat, bins=num_bins)
#
return {'xxx':edges[0], 'yyy':edges[1], 'ccc':stat_result.T}
#
def weighted_quantile(values, quantiles, sample_weight=None, values_sorted=False, old_style=False):
"""
Compute weighted quantiles, handling NaN values.
Parameters:
values (array-like): The data values.
quantiles (array-like): The desired quantiles in [0, 1].
sample_weight (array-like): The weights for each value. If None, each value is assumed to have equal weight.
values_sorted (bool): If True, assumes `values` are sorted.
old_style (bool): If True, use the old-style quantile definition used in numpy <= 1.4.0.
Returns:
numpy.ndarray: The computed weighted quantiles.
"""
values = np.array(values)
quantiles = np.array(quantiles)
if sample_weight is None:
sample_weight = np.ones_like(values)
else:
sample_weight = np.array(sample_weight)
# Remove NaN entries
valid_mask = ~np.isnan(values) & ~np.isnan(sample_weight)
values = values[valid_mask]
sample_weight = sample_weight[valid_mask]
if not values_sorted:
sorter = np.argsort(values)
values = values[sorter]
sample_weight = sample_weight[sorter]
weighted_quantiles = np.cumsum(sample_weight) - 0.5 * sample_weight
weighted_quantiles /= np.sum(sample_weight)
if old_style:
return np.interp(quantiles, weighted_quantiles, values)
else:
return np.interp(quantiles, weighted_quantiles, values, left=values[0], right=values[-1])
#
Scatter plot ∆AOU vs ∆age and linear regression¶
Compute PO tracer average for water mass selection¶
In [9]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Compute PO tracer average for water mass selection')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
# esm_list = ['NorESM2-LM']
simu = 'historical'
tslice = slice('1972', '2013')
for iesm, vesm in enumerate(esm_list):
print('Compute PO tracer for %s...'%vesm)
#________________
# Get data
print(' Get data...')
var = 'po4'
fname_list = [f"{dirshared}{vesm}_{simu}_{var}_{int(year):04}.nc" for year in np.arange(int(tslice.start), int(tslice.stop)+.5)]
# fname = dirshared+vesm+"_"+simu+"_"+var+"_[0-9][0-9][0-9][0-9].nc"
zwds = xr.open_mfdataset(fname_list, **kwopenmfds) # in mol.m-3
zwda1 = zwds[var].sel(year=tslice).mean(dim='year')
var = 'o2'
fname_list = [f"{dirshared}{vesm}_{simu}_{var}_{int(year):04}.nc" for year in np.arange(int(tslice.start), int(tslice.stop)+.5)]
# fname = dirshared+vesm+"_"+simu+"_"+var+"_[0-9][0-9][0-9][0-9].nc"
zwds = xr.open_mfdataset(fname_list, **kwopenmfds) # in mol.m-3
zwda2 = zwds[var].sel(year=tslice).mean(dim='year')
#________________
# Compute PO tracer
print(' Compute PO tracer...')
# zwda_pot = 172*zwda1 + zwda2 # po-tracer = 172*po4+o2
zwda_pot = zwda1 + zwda2/175# po-tracer = po4 + o2/175 Broecker et al. 1991
zwda_pot.name='po-tracer'
#________________
# create dataset
zwda_ds = zwda_pot.to_dataset()
zwda_ds.attrs['description'] = 'PO tracer = PO4 + O2/175 # Broecker et al. 1991'
#________________
# Save in netcdf
print(' Save in netcdf...')
ncname = netcdfdir+vesm+'_'+simu+f'_po_tracer_average_on_{tslice.start}-{tslice.stop}.nc'
zwda_ds.to_netcdf(ncname)
print(' File saved: %s'%ncname)
#
Plot PO-tracer¶
In [17]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Plot PO-tracer')
#--------------------
# Prepare data2plot
#--------------------
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
data2plot={}
ocean_dict = {'Atlantic': (-60, -10), 'Pacific': (-180, -130), 'Indian':(40, 90)}
for iesm, vesm in enumerate(esm_list):
print(vesm+'...')
data2plot[vesm] = {}
print(' Get po-tracer data...')
fname = netcdfdir+vesm+'_historical_po_tracer_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_pot = zwds['po-tracer']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zwda_pot = zwda_pot.isel(j=slice(None, None, -1))
#
#_________________
# Get area
print(' Get area...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello'].load()
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
#________________
# Zonal average on ocean sectors
print(' Zonal average on ocean sectors...')
for iii, (kocean, vocean) in enumerate(ocean_dict.items()):
zwda_avg = zonal_avg(zwda_pot, zarea, longitudes=vocean)
data2plot[vesm][kocean] = {'X' : zwda_avg.latitude.values,
'Y' : zwda_avg.depth.values,
'Z' : zwda_avg.values}
#
#
#--------------------
# Figure parameters
#--------------------
figname = dirout + 'section_po-tracer_average_on_1972-2013.png'
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['Atlantic', 'Pacific', 'Indian']
zfact = 1000.
#______________
# Figure size
nrow, ncol = len(esm_list), len(region_list)
fwidth_cm, fheight_cm = 4*ncol, 3*nrow
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
#______________
# cmap
a = cmcrameri.cm.batlow_r
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_under('grey')
newcmap.set_over(colors[-1])
newcmap.set_bad("none")
#______________
# pcm keywords
kwpcm = dict(cmap=newcmap, vmin=0, vmax=3)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
#______________
# Create figure and axes
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#--------------------
# Plot
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iregion, vregion in enumerate(region_list):
zax = ax[iesm, iregion]
xxx = data2plot[vesm][vregion]['X']
yyy = data2plot[vesm][vregion]['Y']
ccc = data2plot[vesm][vregion]['Z']*zfact - 1.95
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
# zwct = zax.contour(xxx, yyy, ccc, [0.35, 0.91], colors='k')
zwct = zax.contour(xxx, yyy, ccc, [0.91, 1.2], colors='k')
pcm[iesm, iregion] = zwpcm
zax.set_ylim((6000, -10))
zax.set_xlim((-90, 90))
ttl = extended_alphabet_reverse.pop() + f')'
zax.text(-80, 800, ttl)
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.1, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for iregion, vregion in enumerate(region_list) :
zax = ax[0, iregion]
zax.text(.5, 1.2, vregion, transform=zax.transAxes, ha='center', va='bottom')
#
for zax in ax[:, 0]: zax.set_ylabel('Depth [m]')
for zax in ax[-1, :]: zax.set_xlabel('Latitude [Deg. N]')
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, 1].get_position()
nx0 = zw1.x0
ny0 = zw1.y0 - .8*zw1.height
nw = zw2.x1 - nx0
nh = 0.06*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
cbar.set_label(r'$PO^*$ [mmol$\,$PO$_4$ m$^{-3}$]')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Compute density average for water mass selection¶
In [12]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression group by density')
print('## Compute density average for water mass selection')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
simu = 'historical'
tslice = slice('1972', '2013')
for iesm, vesm in enumerate(esm_list):
print('Compute for %s...'%vesm)
#________________
# Get data
print(' Get data...')
var = 'thetao'
fname_list = [f"{dirshared}{vesm}_{simu}_{var}_{int(year):04}.nc" for year in np.arange(int(tslice.start), int(tslice.stop)+.5)]
# fname = dirshared+vesm+"_"+simu+"_"+var+"_[0-9][0-9][0-9][0-9].nc"
zwds = xr.open_mfdataset(fname_list, **kwopenmfds).sel(year=tslice)
zwda_temp = zwds[var]
var = 'so'
fname_list = [f"{dirshared}{vesm}_{simu}_{var}_{int(year):04}.nc" for year in np.arange(int(tslice.start), int(tslice.stop)+.5)]
# fname = dirshared+vesm+"_"+simu+"_"+var+"_[0-9][0-9][0-9][0-9].nc"
zwds = xr.open_mfdataset(fname_list, **kwopenmfds).sel(year=tslice)
zwda_sali = zwds[var]
#________________
# Compute density
print(' Compute density...')
zwda_sigma0 = compute_potential_density_anomaly(zwda_temp, zwda_sali).mean(dim='year')
#________________
# create dataset
zwda_ds = zwda_sigma0.to_dataset()
#________________
# Save in netcdf
print(' Save in netcdf...')
ncname = netcdfdir+f'{vesm}_{simu}_sigma0_average_on_{tslice.start}-{tslice.stop}.nc'
zwda_ds.to_netcdf(ncname)
print(' File saved: %s'%ncname)
#
Test plot PO-tracer in umol per kg¶
In [20]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Test plot PO-tracer in umol per kg')
print('################################')
#--------------------
# Prepare data2plot
#--------------------
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
data2plot={}
ocean_dict = {'Atlantic': (-60, -10), 'Pacific': (-180, -130), 'Indian':(40, 90)}
for iesm, vesm in enumerate(esm_list):
print(vesm+'...')
data2plot[vesm] = {}
print(' Get po-tracer data...')
fname = netcdfdir+vesm+'_historical_po_tracer_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_pot = zwds['po-tracer']
print(' Get density data...')
fname = netcdfdir+vesm+'_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in kg.m-3
zwda_sigma0 = zwds['sigma0'] + 1000.
zwda_pot = zwda_pot/zwda_sigma0.values # mol kg-1
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zwda_pot = zwda_pot.isel(j=slice(None, None, -1))
#
#_________________
# Get area
print(' Get area...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello'].load()
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
#________________
# Zonal average on ocean sectors
print(' Zonal average on ocean sectors...')
for iii, (kocean, vocean) in enumerate(ocean_dict.items()):
zwda_avg = zonal_avg(zwda_pot, zarea, longitudes=vocean)
data2plot[vesm][kocean] = {'X' : zwda_avg.latitude.values,
'Y' : zwda_avg.depth.values,
'Z' : zwda_avg.values}
#
#
#--------------------
# Figure parameters
#--------------------
figname = dirout + 'section_po-tracer_average_on_1972-2013_in_umol_per_kg.png'
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['Atlantic', 'Pacific', 'Indian']
zfact = 1e6 # mol kg-1 to umol kg-1
#______________
# Figure size
nrow, ncol = len(esm_list), len(region_list)
fwidth_cm, fheight_cm = 4*ncol, 3*nrow
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
#______________
# cmap
a = cmcrameri.cm.batlow_r
colors = a(np.linspace(0,1,10+1))
newcmap = ListedColormap(colors[:-1])
newcmap.set_under('grey')
newcmap.set_over(colors[-1])
newcmap.set_bad("none")
#______________
# pcm keywords
kwpcm = dict(cmap=newcmap, vmin=0, vmax=3)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
#______________
# Create figure and axes
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#--------------------
# Plot
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iregion, vregion in enumerate(region_list):
zax = ax[iesm, iregion]
xxx = data2plot[vesm][vregion]['X']
yyy = data2plot[vesm][vregion]['Y']
ccc = data2plot[vesm][vregion]['Z']*zfact - 1.95
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
# zwct = zax.contour(xxx, yyy, ccc, [0.35, 0.91], colors='k')
# zwct = zax.contour(xxx, yyy, ccc, [0.91, 1.2], colors='k')
zwct = zax.contour(xxx, yyy, ccc, [0.91], colors='k')
zwct = zax.contour(xxx, yyy, ccc, [1.2], colors='w', linestyles='--')
pcm[iesm, iregion] = zwpcm
zax.set_ylim((6000, -10))
zax.set_xlim((-90, 90))
ttl = extended_alphabet_reverse.pop() + f')'
zax.text(-80, 800, ttl)
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.1, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for iregion, vregion in enumerate(region_list) :
zax = ax[0, iregion]
zax.text(.5, 1.2, vregion, transform=zax.transAxes, ha='center', va='bottom')
#
for zax in ax[:, 0]: zax.set_ylabel('Depth [m]')
for zax in ax[-1, :]: zax.set_xlabel('Latitude [Deg. N]')
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, 1].get_position()
nx0 = zw1.x0
ny0 = zw1.y0 - .8*zw1.height
nw = zw2.x1 - nx0
nh = 0.06*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
cbar.set_label(r'$PO^*$ [mmol$\,$PO$_4$ m$^{-3}$]')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Define function prep_data2plot¶
In [10]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Define function prep_data2plot')
def prep_data2plot(zaou, zage, zcond, zvolume, zomask, zcoloring_dict={}):
print('> prep_data2plot...')
zdata2plot = {}
#_________________
# Select water mass
print('>> Select water mass...')
aou_in_water_mass = zaou.where(zcond, np.nan)
age_in_water_mass = zage.where(zcond, np.nan)
volume_in_water_mass = zvolume.where(zcond, np.nan)
#_________________
# Compute volume and number of cell
print('>> Compute volume and number of cell')
zdata2plot['volume'] = volume_in_water_mass.sum().values
zdata2plot['cells'] = zcond.sum().values
#_________________
# Compute maps of water mass distribution
print('>> Compute water mass distribution')
zdata2plot['water mass distribution'] = {}
zw = (xr.where(zcond, 1, 0)).where(zomask.values)
zvertical = compute_vertical_average(zw)
zdata2plot['water mass distribution']['vertical average'] = {
'xxx': zvertical.longitude.values,
'yyy': zvertical.latitude.values,
'ccc': zvertical.values
}
zzonal = zonal_avg(zw, zvolume)
zdata2plot['water mass distribution']['zonal average'] = {
'xxx': zzonal.latitude.values,
'yyy': zzonal.depth.values,
'ccc': zzonal.values
}
#_________________
# Compute integrated trends
print('>> Compute integrated trends')
zdata2plot['integrated aou trend'] = (aou_in_water_mass*volume_in_water_mass).sum(skipna=True).values
zdata2plot['integrated age trend'] = (age_in_water_mass*volume_in_water_mass).sum(skipna=True).values
#_________________
# Compute histograms 2D for entire selected water mass
print('>> Compute histogram 2D')
nbins1 = 200
bounds = [[np.nanmin(age_in_water_mass.values.flatten()),
np.nanmax(age_in_water_mass.values.flatten())],
[np.nanmin(aou_in_water_mass.values.flatten()),
np.nanmax(aou_in_water_mass.values.flatten())]]
hhh = histogram2d(age_in_water_mass.values.flatten(), aou_in_water_mass.values.flatten(), \
range=bounds, bins=nbins1).T # need to transpose for visualisation purpose
xxx = np.linspace(bounds[0][0], bounds[0][1], nbins1)
yyy = np.linspace(bounds[1][0], bounds[1][1], nbins1)
zdata2plot['histogram2d'] = {'xxx':xxx, 'yyy':yyy, 'ccc':hhh}
#_______________
# compute linear regression
print('>> Compute linear regression...')
X = age_in_water_mass.values.flatten()
Y = aou_in_water_mass.values.flatten()
index = ~(np.isnan(X) | np.isnan(Y))
X = X[index]
Y = Y[index]
results = stats.linregress(X, Y)
Y_predicted = results.intercept + results.slope * X
nnn = len(X) # Number of observations
t_value = stats.t.ppf(1 - 0.025, nnn - 2) # t-value for 95% confidence interval with n-2 degrees of freedom
# Standard error of the predicted y-values
s_err = np.sqrt(np.sum((Y - Y_predicted) ** 2) / (nnn - 2))
# nb: results.stderr is s_err/np.rmse(X)
#_______________
# Compute 95% confidence band
# Subsampled X and Y predicted to compute 95% confidence band
combined = list(zip(X, Y_predicted))
if nnn>100: sample_size=100
else: sample_size=nnn
# Find the minimum and maximum elements based on the `y` values
min_combined = min(combined, key=lambda pair: pair[0])
max_combined = max(combined, key=lambda pair: pair[0])
# Remove these elements from the combined list to sample the rest
remaining_combined = [pair for pair in combined if pair != min_combined and pair != max_combined]
# Sample the remaining elements
remaining_sample_size = sample_size - 2
import random
sampled_remaining = random.sample(remaining_combined, remaining_sample_size)
# Include the min and max elements in the final sample
sampled_combined = [min_combined] + sampled_remaining + [max_combined]
sampled_X, sampled_Y_predicted = zip(*sampled_combined)
# Convert the sampled tuples back to lists (if needed)
sampled_X = list(sampled_X)
sampled_Y_predicted = list(sampled_Y_predicted)
# Combine `xxx` and `yyy` and sort based on `xxx`
xxx_yyy = sorted(zip(sampled_X, sampled_Y_predicted))
# Unzip the sorted pairs
sorted_xxx, sorted_yyy = zip(*xxx_yyy)
sampled_X = np.array(sorted_xxx)
sampled_Y_predicted = np.array(sorted_yyy)
conf_interval = t_value * s_err * np.sqrt(1 / nnn + (sampled_X - np.mean(X))**2 / np.sum((X - np.mean(X))**2))
lower_band = sampled_Y_predicted - conf_interval
upper_band = sampled_Y_predicted + conf_interval
pred_interval = t_value * s_err * np.sqrt(1 + 1 / nnn + (sampled_X - np.mean(X))**2 / np.sum((X - np.mean(X))**2))
lower_pred_band = sampled_Y_predicted - pred_interval
upper_pred_band = sampled_Y_predicted + pred_interval
del X, Y
gc.collect()
#_______________
# Save linregress analysis
zdata2plot['linregress'] = {}
zdata2plot['linregress']['slope'] = results.slope
zdata2plot['linregress']['intercept'] = results.intercept
zdata2plot['linregress']['rvalue'] = results.rvalue
zdata2plot['linregress']['pvalue'] = results.pvalue
zdata2plot['linregress']['stderr'] = results.stderr
zdata2plot['linregress']['intercept_stderr'] = results.intercept_stderr
zdata2plot['linregress']['number of observation'] = nnn
zdata2plot['linregress']['tvalue'] = t_value
zdata2plot['linregress']['sampled_X'] = sampled_X
zdata2plot['linregress']['sampled_Y_predicted'] = sampled_Y_predicted
zdata2plot['linregress']['lower_band'] = lower_band
zdata2plot['linregress']['upper_band'] = upper_band
zdata2plot['linregress']['conf_interval'] = conf_interval
zdata2plot['linregress']['stderr_pred'] = s_err
zdata2plot['linregress']['lower_pred_band'] = lower_pred_band
zdata2plot['linregress']['upper_pred_band'] = upper_pred_band
zdata2plot['linregress']['conf_interval'] = pred_interval
# xxx = np.array([age_in_water_mass.min(), age_in_water_mass.max()])
# yyy = slope * xxx + intercept
# zdata2plot['linregress']['line'] = {'xxx':xxx, 'yyy':yyy}
#_______________
# Compute mean of zcoloring_list
if len(zcoloring_dict)>0:
print('>> Compute mean of zcoloring_list...')
for kcoloring, vcoloring in zcoloring_dict.items():
print(f'>>> for {kcoloring}...')
coloring_in_water_mass = vcoloring.where(zcond, np.nan)
zdata2plot[kcoloring] = coloring_2D_bins(age_in_water_mass.values, aou_in_water_mass.values, \
coloring_in_water_mass.values, nbin=nbins1)
#
#
#_______________
# Compute prediction and prediction error
X3D = age_in_water_mass.values
Y3D = aou_in_water_mass.values
Y3D_predicted = results.intercept + results.slope * X3D
Y3D_error = Y3D - Y3D_predicted
Y3D_abserror = np.abs(Y3D - Y3D_predicted)
x3dmean = np.nanmean(X3D)
CI3D = t_value * s_err * np.sqrt(1 / nnn + (X3D - x3dmean)**2 / np.nansum((X3D - x3dmean)**2))
# Create new DataArray
aou_in_water_mass_predicted = xr.DataArray(
Y3D_predicted,
dims=aou_in_water_mass.dims,
coords=aou_in_water_mass.coords,
attrs=aou_in_water_mass.attrs
)
aou_in_water_mass_predicted.name = aou_in_water_mass.name
aou_in_water_mass_error = xr.DataArray(
Y3D_error,
dims=aou_in_water_mass.dims,
coords=aou_in_water_mass.coords,
attrs=aou_in_water_mass.attrs
)
aou_in_water_mass_error.name = aou_in_water_mass.name
aou_in_water_mass_abserror = xr.DataArray(
Y3D_abserror,
dims=aou_in_water_mass.dims,
coords=aou_in_water_mass.coords,
attrs=aou_in_water_mass.attrs
)
aou_in_water_mass_abserror.name = aou_in_water_mass.name
aou_in_water_mass_pred_confint = xr.DataArray(
CI3D,
dims=aou_in_water_mass.dims,
coords=aou_in_water_mass.coords,
attrs=aou_in_water_mass.attrs
)
aou_in_water_mass_pred_confint.name = aou_in_water_mass.name
zdata2plot['aou prediction'] = {}
#_______________
# Save prediction
zdata2plot['aou prediction']['predicted'] = {}
zdata2plot['aou prediction']['predicted']['3D'] = {
'xxx': aou_in_water_mass.longitude.values,
'yyy': aou_in_water_mass.latitude.values,
'zzz': aou_in_water_mass.depth.values,
'ccc': Y3D_predicted
}
zvertical = compute_vertical_average(aou_in_water_mass_predicted)
zdata2plot['aou prediction']['predicted']['vertical average'] = {
'xxx': zvertical.longitude.values,
'yyy': zvertical.latitude.values,
'ccc': zvertical.values
}
zzonal = zonal_avg(aou_in_water_mass_predicted, zvolume)
zdata2plot['aou prediction']['predicted']['zonal average'] = {
'xxx': zzonal.latitude.values,
'yyy': zzonal.depth.values,
'ccc': zzonal.values
}
#_______________
# Save prediction error
zdata2plot['aou prediction']['error'] = {}
zdata2plot['aou prediction']['error']['3D'] = {
'xxx': aou_in_water_mass.longitude.values,
'yyy': aou_in_water_mass.latitude.values,
'zzz': aou_in_water_mass.depth.values,
'ccc': Y3D_error
}
zvertical = compute_vertical_average(aou_in_water_mass_error)
zdata2plot['aou prediction']['error']['vertical average'] = {
'xxx': zvertical.longitude.values,
'yyy': zvertical.latitude.values,
'ccc': zvertical.values
}
zzonal = zonal_avg(aou_in_water_mass_error, zvolume)
zdata2plot['aou prediction']['error']['zonal average'] = {
'xxx': zzonal.latitude.values,
'yyy': zzonal.depth.values,
'ccc': zzonal.values
}
#_______________
# Save prediction absolute error
zdata2plot['aou prediction']['absolute error'] = {}
zdata2plot['aou prediction']['absolute error']['3D'] = {
'xxx': aou_in_water_mass.longitude.values,
'yyy': aou_in_water_mass.latitude.values,
'zzz': aou_in_water_mass.depth.values,
'ccc': Y3D_abserror
}
zvertical = compute_vertical_average(aou_in_water_mass_abserror)
zdata2plot['aou prediction']['absolute error']['vertical average'] = {
'xxx': zvertical.longitude.values,
'yyy': zvertical.latitude.values,
'ccc': zvertical.values
}
zzonal = zonal_avg(aou_in_water_mass_abserror, zvolume)
zdata2plot['aou prediction']['absolute error']['zonal average'] = {
'xxx': zzonal.latitude.values,
'yyy': zzonal.depth.values,
'ccc': zzonal.values
}
#_______________
# Save confidence interval of prediction
zdata2plot['aou prediction']['confidence interval'] = {}
zdata2plot['aou prediction']['confidence interval']['3D'] = {
'xxx': aou_in_water_mass.longitude.values,
'yyy': aou_in_water_mass.latitude.values,
'zzz': aou_in_water_mass.depth.values,
'ccc': CI3D
}
zvertical = compute_vertical_average(aou_in_water_mass_pred_confint)
zdata2plot['aou prediction']['confidence interval']['vertical average'] = {
'xxx': zvertical.longitude.values,
'yyy': zvertical.latitude.values,
'ccc': zvertical.values
}
zzonal = zonal_avg(aou_in_water_mass_pred_confint, zvolume)
zdata2plot['aou prediction']['confidence interval']['zonal average'] = {
'xxx': zzonal.latitude.values,
'yyy': zzonal.depth.values,
'ccc': zzonal.values
}
return zdata2plot
#
#----------------
# End define prep_data2plot
#----------------
Prepare data2plot¶
In [11]:
%%time
%%memit -c
# ca. 17min
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
simu = 'historical'
tperiod = '1972-2013'
# simu = 'ssp585'
# tperiod = '2015-2099'
zfact_esm_aou = 1000. # mol.m-3 to mmol.m-3 = umol.l-1
number_of_years_in_picontrol = 2099-1850
#----------------
# Loop on ESM
#----------------
data2plot = {}
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot[vesm] = {}
#----------------
# Get data
#----------------
print(' Get aou data...')
fname = dirshared+vesm+'_'+simu+'_'+'aou_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_aou = zwds['slope']*zfact_esm_aou
zwda_aou_pval = zwds['pvalue']
print(' Get age data...')
fname = dirshared+vesm+'_'+simu+'_'+'agessc_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_age = zwds['slope'] # yr.yr-1
zwda_age_pval = zwds['pvalue']
print(' Get density data...')
fname = netcdfdir+vesm+'_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_sigma0 = zwds['sigma0']
print(' Get po-tracer data...')
fname = netcdfdir+vesm+'_historical_po_tracer_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_pot = zwds['po-tracer']*1000 - 1.95
# FOR MPI model maybe
if vesm == 'MPI-ESM1-2-LR' :
zwda_aou = zwda_aou.isel(j=slice(None, None, -1))
zwda_age = zwda_age.isel(j=slice(None, None, -1))
zwda_aou_pval = zwda_aou_pval.isel(j=slice(None, None, -1))
zwda_age_pval = zwda_age_pval.isel(j=slice(None, None, -1))
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
zwda_pot = zwda_pot.isel(j=slice(None, None, -1))
#
#----------------
# Compute volume
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask')
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zarea
#----------------
# Align coordinates
#----------------
print(' Align coordinates...')
# use reindex if the coordinates match but are not aligned
for zwcoords in zwda_aou.coords :
if zwcoords in zwda_age.coords: zwda_age[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_aou_pval.coords: zwda_aou_pval[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_age_pval.coords: zwda_age_pval[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_sigma0.coords: zwda_sigma0[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_pot.coords: zwda_pot[zwcoords] = zwda_aou[zwcoords]
if zwcoords in omask.coords: omask[zwcoords] = zwda_aou[zwcoords]
if zwcoords in volume.coords: volume[zwcoords] = zwda_aou[zwcoords]
#
#----------------
# Define coloring dict
#----------------
print(' Define coloring dict...')
dep3D = expand_coords(zwda_sigma0, 'depth')
lat3D = expand_coords(zwda_sigma0, 'latitude')
lon3D = expand_coords(zwda_sigma0, 'longitude')
coloring_dict = {'depth':dep3D, 'latitude':lat3D, 'longitude':lon3D, 'sigma0':zwda_sigma0}
#----------------
# In different water mass
#----------------
#_________________
# Total ocean
print(' For total ocean...')
COND_total = omask
data2plot[vesm]['total'] = prep_data2plot(zwda_aou, zwda_age, COND_total, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Deep ocean
print(' For deep ocean...')
COND_deep = COND_total & (dep3D > 1000)
data2plot[vesm]['deep'] = prep_data2plot(zwda_aou, zwda_age, COND_deep, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Deep ocean significant
print(' For deep ocean significant...')
COND_deep_signif = COND_deep & (zwda_aou_pval<=.05) & (zwda_age_pval<=.05)
data2plot[vesm]['deep significant'] = prep_data2plot(zwda_aou, zwda_age, COND_deep_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern
print(' For southern waters...')
po_tracer_trshld = [0.91, 2.]
COND_southern = COND_deep & (lat3D < 60) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
COND_southern_signif = COND_deep_signif & (lat3D < 60) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
data2plot[vesm]['southern'] = prep_data2plot(zwda_aou, zwda_age, COND_southern_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Density quantile for Southern waters
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_southern, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_southern, np.nan).values.flatten())
#_________________
# Southern densest
print(' For southern dense waters...')
COND_southern_dense = COND_southern_signif & (zwda_sigma0 >= quantiles[5])
data2plot[vesm]['southern dense'] = prep_data2plot(zwda_aou, zwda_age, COND_southern_dense, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern lightest
print(' For southern light waters...')
COND_southern_light = COND_southern_signif & (zwda_sigma0 < quantiles[5])
data2plot[vesm]['southern light'] = prep_data2plot(zwda_aou, zwda_age, COND_southern_light, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Atlantic
print(' For atlantic waters...')
po_tracer_trshld = [0.35, 0.91]
COND_atlantic = COND_deep & (lat3D < 60) & (lon3D>=-90) & (lon3D<=30) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
COND_atlantic_signif = COND_deep_signif & (lat3D < 60) & (lon3D>=-90) & (lon3D<=30) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
data2plot[vesm]['atlantic'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Density quantile for Atlanntic waters
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_atlantic, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_atlantic, np.nan).values.flatten())
#_________________
# Atlantic densest
print(' For atlantic dense waters...')
COND_atlantic_dense = COND_atlantic_signif & (zwda_sigma0 >= quantiles[5])
data2plot[vesm]['atlantic dense'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic_dense, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Atlantic lightest
print(' For atlantic light waters...')
COND_atlantic_light = COND_atlantic_signif & (zwda_sigma0 < quantiles[5])
data2plot[vesm]['atlantic light'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic_light, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern and Atlantic water mass
print(' For southern and atlantic water mass...')
COND_atl_south = COND_atlantic_signif | COND_southern_signif
data2plot[vesm]['southern and atlantic'] = prep_data2plot(zwda_aou, zwda_age, COND_atl_south, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# The rest of deep and significant
print(' For the rest of deep and significant...')
COND_rest_deep_signif = COND_deep_signif & ~COND_atl_south
data2plot[vesm]['reminder deep and significant'] = prep_data2plot(zwda_aou, zwda_age, COND_rest_deep_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# The deep and non significant
print(' For the deep and non significant...')
COND_deep_non_signif = COND_deep & ~COND_deep_signif
data2plot[vesm]['deep and non significant'] = prep_data2plot(zwda_aou, zwda_age, COND_deep_non_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# The surface
print(' For the surface...')
COND_surf = COND_total & ~COND_deep
data2plot[vesm]['surface'] = prep_data2plot(zwda_aou, zwda_age, COND_surf, volume, omask, zcoloring_dict=coloring_dict)
#
#_________________
# Save
print('Save file...')
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+datetime.datetime.now())
print('################################')
print('################################')
Prepare data2plot but just to print density thresholds¶
In [22]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Prepare data2plot but just to print density thresholds')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
simu = 'historical'
tperiod = '1972-2013'
# simu = 'ssp585'
# tperiod = '2015-2099'
zfact_esm_aou = 1000. # mol.m-3 to mmol.m-3 = umol.l-1
number_of_years_in_picontrol = 2099-1850
#----------------
# Loop on ESM
#----------------
data2plot = {}
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Get density thresholds for %s...'%vesm)
print('-----------------------')
data2plot[vesm] = {}
#----------------
# Get data
#----------------
fname = dirshared+vesm+'_'+simu+'_'+'aou_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_aou = zwds['slope']*zfact_esm_aou
zwda_aou_pval = zwds['pvalue']
fname = dirshared+vesm+'_'+simu+'_'+'agessc_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_age = zwds['slope'] # yr.yr-1
zwda_age_pval = zwds['pvalue']
fname = netcdfdir+vesm+'_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in kg.m-3
zwda_sigma0 = zwds['sigma0']
fname = netcdfdir+vesm+'_historical_po_tracer_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_pot = zwds['po-tracer']*1000 - 1.95
# FOR MPI model maybe
if vesm == 'MPI-ESM1-2-LR' :
zwda_aou = zwda_aou.isel(j=slice(None, None, -1))
zwda_age = zwda_age.isel(j=slice(None, None, -1))
zwda_aou_pval = zwda_aou_pval.isel(j=slice(None, None, -1))
zwda_age_pval = zwda_age_pval.isel(j=slice(None, None, -1))
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
zwda_pot = zwda_pot.isel(j=slice(None, None, -1))
#
#----------------
# Compute volume
#----------------
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask')
volume = omask * depthickness * zarea
del depbounds, depthickness
#----------------
# Align coordinates
#----------------
# use reindex if the coordinates match but are not aligned
for zwcoords in zwda_aou.coords :
if zwcoords in zwda_age.coords: zwda_age[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_aou_pval.coords: zwda_aou_pval[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_age_pval.coords: zwda_age_pval[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_sigma0.coords: zwda_sigma0[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_pot.coords: zwda_pot[zwcoords] = zwda_aou[zwcoords]
if zwcoords in omask.coords: omask[zwcoords] = zwda_aou[zwcoords]
if zwcoords in volume.coords: volume[zwcoords] = zwda_aou[zwcoords]
#
#----------------
# Define dep, lat, lon 3D
#----------------
dep3D = expand_coords(zwda_sigma0, 'depth')
lat3D = expand_coords(zwda_sigma0, 'latitude')
lon3D = expand_coords(zwda_sigma0, 'longitude')
#----------------
# In different water mass significant
#----------------
# print('\n >>> Only significant point <<<')
# COND_total = omask
# COND_deep = COND_total & (dep3D > 1000)
# COND_deep_signif = COND_deep & (zwda_aou_pval<=.05) & (zwda_age_pval<=.05)
# po_tracer_trshld = [0.91, 2.]
# #_________________
# # Southern waters
# COND_southern = COND_deep_signif & (lat3D < 60) & \
# (zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
# quantiles = weighted_quantile(values=zwda_sigma0.where(COND_southern, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
# sample_weight=volume.where(COND_southern, np.nan).values.flatten())
# print(f' Southern light waters: sigma0 in ]{quantiles[5]:.2f}, {quantiles[0]:.2f}]')
# print(f' Southern dense waters: sigma0 in [{quantiles[-1]:.2f}, {quantiles[5]:.2f}]')
# #_________________
# # Atlantic
# po_tracer_trshld = [0.35, 0.91]
# COND_atlantic = COND_deep_signif & (lat3D < 60) & (lon3D>=-90) & (lon3D<=30) & \
# (zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
# quantiles = weighted_quantile(values=zwda_sigma0.where(COND_atlantic, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
# sample_weight=volume.where(COND_atlantic, np.nan).values.flatten())
# print(f' Atlantic light waters: sigma0 in ]{quantiles[5]:.2f}, {quantiles[0]:.2f}]')
# print(f' Atlantic dense waters: sigma0 in [{quantiles[-1]:.2f}, {quantiles[5]:.2f}]')
#----------------
# In different water mass significant
#----------------
print('\n >>> Including non-significant point <<<')
COND_total = omask
COND_deep = COND_total & (dep3D > 1000)
#_________________
# Southern waters
po_tracer_trshld = [0.91, 2.]
COND_southern = COND_deep & (lat3D < 60) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_southern, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_southern, np.nan).values.flatten())
print(f' Southern dense waters: sigma0 in [{quantiles[-1]:.2f}, {quantiles[5]:.2f}]')
print(f' Southern light waters: sigma0 in ]{quantiles[5]:.2f}, {quantiles[0]:.2f}]')
#_________________
# Atlantic
po_tracer_trshld = [0.35, 0.91]
COND_atlantic = COND_deep & (lat3D < 60) & (lon3D>=-90) & (lon3D<=30) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_atlantic, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_atlantic, np.nan).values.flatten())
print(f' Atlantic dense waters: sigma0 in [{quantiles[-1]:.2f}, {quantiles[5]:.2f}]')
print(f' Atlantic light waters: sigma0 in ]{quantiles[5]:.2f}, {quantiles[0]:.2f}]')
print('\n')
#
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Including low significance point¶
Prepare data2plot¶
In [ ]:
%%time
%%memit -c
# ca. 10min
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Including low significance point')
print('### Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
simu = 'historical'
tperiod = '1972-2013'
# simu = 'ssp585'
# tperiod = '2015-2099'
zfact_esm_aou = 1000. # mol.m-3 to mmol.m-3 = umol.l-1
number_of_years_in_picontrol = 2099-1850
#----------------
# Loop on ESM
#----------------
data2plot = {}
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot[vesm] = {}
#----------------
# Get data
#----------------
print(' Get aou data...')
fname = dirshared+vesm+'_'+simu+'_'+'aou_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_aou = zwds['slope']*zfact_esm_aou
print(' Get age data...')
fname = dirshared+vesm+'_'+simu+'_'+'agessc_linregress_on_'+tperiod+'_drift_removed.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_age = zwds['slope'] # yr.yr-1
print(' Get density data...')
fname = netcdfdir+vesm+'_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_sigma0 = zwds['sigma0']
print(' Get po-tracer data...')
fname = netcdfdir+vesm+'_historical_po_tracer_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds) # in mol.m-3
zwda_pot = zwds['po-tracer']*1000 - 1.95
# FOR MPI model maybe
if vesm == 'MPI-ESM1-2-LR' :
zwda_aou = zwda_aou.isel(j=slice(None, None, -1))
zwda_age = zwda_age.isel(j=slice(None, None, -1))
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
zwda_pot = zwda_pot.isel(j=slice(None, None, -1))
#
#----------------
# Compute volume
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask')
volume = omask * depthickness * zarea
del depbounds, depthickness
#----------------
# Align coordinates
#----------------
print(' Align coordinates...')
# use reindex if the coordinates match but are not aligned
for zwcoords in zwda_aou.coords :
if zwcoords in zwda_age.coords: zwda_age[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_sigma0.coords: zwda_sigma0[zwcoords] = zwda_aou[zwcoords]
if zwcoords in zwda_pot.coords: zwda_pot[zwcoords] = zwda_aou[zwcoords]
if zwcoords in omask.coords: omask[zwcoords] = zwda_aou[zwcoords]
if zwcoords in volume.coords: volume[zwcoords] = zwda_aou[zwcoords]
#
#----------------
# Define coloring dict
#----------------
print(' Define coloring dict...')
dep3D = expand_coords(zwda_sigma0, 'depth')
lat3D = expand_coords(zwda_sigma0, 'latitude')
lon3D = expand_coords(zwda_sigma0, 'longitude')
coloring_dict = {'depth':dep3D, 'latitude':lat3D, 'longitude':lon3D, 'sigma0':zwda_sigma0}
#----------------
# In different water mass
#----------------
#_________________
# Total ocean
print(' For total ocean...')
COND_total = omask
data2plot[vesm]['total'] = prep_data2plot(zwda_aou, zwda_age, COND_total, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Deep ocean
print(' For deep ocean...')
COND_deep = COND_total & (dep3D > 1000)
data2plot[vesm]['deep'] = prep_data2plot(zwda_aou, zwda_age, COND_deep, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern
print(' For southern waters...')
po_tracer_trshld = [0.91, 2]
COND_southern = COND_deep & (lat3D < 60) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
data2plot[vesm]['southern'] = prep_data2plot(zwda_aou, zwda_age, COND_southern, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Density quantile for Southern waters
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_southern, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_southern, np.nan).values.flatten())
#_________________
# Southern densest
print(' For southern dense waters...')
COND_southern_dense = COND_southern & (zwda_sigma0 >= quantiles[5])
data2plot[vesm]['southern dense'] = prep_data2plot(zwda_aou, zwda_age, COND_southern_dense, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern lightest
print(' For southern light waters...')
COND_southern_light = COND_southern & (zwda_sigma0 < quantiles[5])
data2plot[vesm]['southern light'] = prep_data2plot(zwda_aou, zwda_age, COND_southern_light, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Atlantic
print(' For atlantic waters...')
po_tracer_trshld = [0.35, 0.91]
COND_atlantic = COND_deep & (lat3D < 60) & (lon3D>=-90) & (lon3D<=30) & \
(zwda_pot > po_tracer_trshld[0]) & (zwda_pot <= po_tracer_trshld[1])
data2plot[vesm]['atlantic'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Density quantile for Atlanntic waters
quantiles = weighted_quantile(values=zwda_sigma0.where(COND_atlantic, np.nan).values.flatten(), quantiles=np.linspace(0, 1, 11), \
sample_weight=volume.where(COND_atlantic, np.nan).values.flatten())
#_________________
# Atlantic densest
print(' For atlantic dense waters...')
COND_atlantic_dense = COND_atlantic & (zwda_sigma0 >= quantiles[5])
data2plot[vesm]['atlantic dense'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic_dense, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Atlantic lightest
print(' For atlantic light waters...')
COND_atlantic_light = COND_atlantic & (zwda_sigma0 < quantiles[5])
data2plot[vesm]['atlantic light'] = prep_data2plot(zwda_aou, zwda_age, COND_atlantic_light, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# Southern and Atlantic water mass
print(' For southern and atlantic water mass...')
COND_atl_south = COND_atlantic | COND_southern
data2plot[vesm]['southern and atlantic'] = prep_data2plot(zwda_aou, zwda_age, COND_atl_south, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# The rest of deep
print(' For the rest of deep...')
COND_rest_deep_signif = COND_deep & ~COND_atl_south
data2plot[vesm]['reminder deep'] = prep_data2plot(zwda_aou, zwda_age, COND_rest_deep_signif, volume, omask, zcoloring_dict=coloring_dict)
#_________________
# The surface
print(' For the surface...')
COND_surf = COND_total & ~COND_deep
data2plot[vesm]['surface'] = prep_data2plot(zwda_aou, zwda_age, COND_surf, volume, omask, zcoloring_dict=coloring_dict)
#
#_________________
# Save
print('Save file...')
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+ str(datetime.datetime.now()))
print('################################')
print('################################')
Plot: scatter plot¶
In [30]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Plot: scatter plot')
#____________________
# Load data2plot
simu = 'historical'
tperiod = '1972-2013'
# simu = 'ssp585'
# tperiod = '2015-2099'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
# Open the pickle file in binary read mode
with open(savefile, 'rb') as file:
# Load the dictionary from the pickle file
data2plot = pickle.load(file)
#
#--------------------
# Figure parameters
#--------------------
#____________________
# figname
figname = savefile.replace('data2plot', 'scatter_plot').replace('.pckl', '.png')
#____________________
# list
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['atlantic light', 'atlantic dense', 'southern light', 'southern dense']
#____________________
# Figure
fwidth_cm, fheight_cm = 17, 11.3333
nrow, ncol = 2, 3
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#____________________
# labels
region_label_dict = {
'southern dense': 'Southern\ndense waters',
'southern light': 'Southern\nlight waters',
'atlantic dense': 'Atlantic\ndense waters',
'atlantic light': 'Atlantic\nlight waters',
}
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
#____________________
# Colors and maps
a = cmcrameri.cm.lajolla
colors = a(np.linspace(0,1,250))
region_color_dict = {
'southern dense': colors[50],
'southern light': colors[50*2],
'atlantic dense': colors[50*3],
'atlantic light': colors[50*4]
}
#______________
# cmap
a = cmcrameri.cm.devon
colors = a(np.linspace(0,1,200))
newcmap = ListedColormap(colors[50:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[49])
newcmap.set_bad("none")
# a = cm.Wistia
# colors = a(np.linspace(0,1,300))
# newcmap = ListedColormap(colors[30:-1])
# newcmap.set_over(colors[-1])
# newcmap.set_under(colors[29])
# newcmap.set_bad("none")
#____________________
# keywords args
kwpcm = {
'southern and atlantic': dict(cmap = newcmap, norm=mpl.colors.LogNorm(vmin=1, vmax=1000)),
'total': dict(cmap=ListedColormap(['#D3D3D3']), norm=mpl.colors.LogNorm(vmin=1, vmax=1000))
}
xlim = (-10, 10)
ylim = ( -3, 3)
#--------------------
# Plot
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
zax = ax.flat[iesm]
zax.axhline(0, lw=.5, c='k')
zax.axvline(0, lw=.5, c='k')
ttl = alphabet_reverse.pop()+') '+esm_name_dict[vesm]
zax.set_title(ttl, loc='left')
zax.set_xlim(xlim)
zax.set_ylim(ylim)
#____________________
# Full scatter plot in background
sel_scatter = 'total'
xxx = data2plot[vesm][sel_scatter]['histogram2d']['xxx']
yyy = data2plot[vesm][sel_scatter]['histogram2d']['yyy']
ccc = data2plot[vesm][sel_scatter]['histogram2d']['ccc']
zax.pcolormesh(xxx, yyy, ccc, **kwpcm[sel_scatter])
#____________________
# Selected scatter plot
sel_scatter = 'southern and atlantic'
xxx = data2plot[vesm][sel_scatter]['histogram2d']['xxx']
yyy = data2plot[vesm][sel_scatter]['histogram2d']['yyy']
ccc = data2plot[vesm][sel_scatter]['histogram2d']['ccc']
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm[sel_scatter])
pcm.flat[iesm] = zwpcm
#____________________
# Add regression lines
lines = []
tab_row1 = []
tab_row2 = []
tab_row3 = []
for region in region_list:
xxx = data2plot[vesm][region]['linregress']['sampled_X']
yyy = data2plot[vesm][region]['linregress']['sampled_Y_predicted']
yyymin = data2plot[vesm][region]['linregress']['lower_band']
yyymax = data2plot[vesm][region]['linregress']['upper_band']
# yyymin = data2plot[vesm][region]['linregress']['lower_pred_band']
# yyymax = data2plot[vesm][region]['linregress']['upper_pred_band']
zax.fill_between(xxx, yyymin, yyymax, color=region_color_dict[region], alpha=0.5, lw=0)
ll, = zax.plot(xxx, yyy, label=region_label_dict[region], color=region_color_dict[region], lw=1)
lines.append(ll)
#____________________
# Save some notes for table
slope = data2plot[vesm][region]['linregress']['slope']
rvalue = data2plot[vesm][region]['linregress']['rvalue']
# ocean_frac = data2plot[vesm][region]['volume']/data2plot[vesm]['total']['volume']*100
ocean_frac = data2plot[vesm][region]['volume']/data2plot[vesm]['deep']['volume']*100
tab_row1.append(f'{np.floor(rvalue**2*100)/100:.2f}')
tab_row2.append(f'{ocean_frac:.0f}%')
tab_row3.append(f'{slope:.2f}')
print(f">>> {vesm}, {region}: {data2plot[vesm][region]['linregress']['pvalue']:.3f}")
#
#____________________
# add notes
kwtext = dict(ha='center', va='center_baseline', fontsize=6)
# kwtext = dict(ha='center', va='top', fontsize=6)
ytxt = .35 #.97
xtxt1 = .5+.09
xtxt2 = xtxt1+.15
xtxt3 = xtxt2+.15
zax.text(xtxt1, ytxt, r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$', transform=zax.transAxes, color='k', **kwtext)
# zax.text(xtxt1, ytxt, r'Slope', transform=zax.transAxes, color='k', **kwtext)
zax.text(xtxt2, ytxt, '$R^2$' , transform=zax.transAxes, color='k', **kwtext)
zax.text(xtxt3, ytxt, 'Ocean\nfraction', transform=zax.transAxes, color='k', **kwtext)
ytxt-=.1
for iregion, vregion in enumerate(region_list):
zax.text(xtxt2, ytxt, tab_row1[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
zax.text(xtxt3, ytxt, tab_row2[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
zax.text(xtxt1, ytxt, tab_row3[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
ytxt-=.07
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
ax.flat[-1].axis('off')
#____________________
# legend
handles = [plt.Line2D([0], [0], marker='None', linestyle='-', label=region_label_dict[region], color=region_color_dict[region], lw=2) for region in region_list]
zax = ax.flat[-1]
zax.legend(handles=handles, handlelength=0.5, frameon=False, handletextpad=1, loc='upper right', bbox_to_anchor=(1, 1))
#____________________
# axis labels
for zax in ax[: , 0]: zax.set_ylabel(f'AOU trends, {tperiod}\n'+r'[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]')
for zax in ax[-1, :]: zax.set_xlabel(f'Age trends, {tperiod} '+r'[yr yr$^{-1}$]')
ax[0, -1].set_xlabel(f'Age trends, {tperiod} '+r'[yr yr$^{-1}$]')
#---------------------
# Colorbar
#---------------------
zw1 = ax.flat[-2].get_position()
nx0 = zw1.x1 + .2*zw1.width
ny0 = zw1.y0
nh = zw1.height
nw = 0.03*nh
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='vertical', \
ticklocation='right', extend='max')
cbar.set_label(r'Number of data point')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Plot: scatter plot include low significance point¶
In [14]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Plot: scatter plot include low significance point')
#____________________
# Load data2plot
# simu = 'historical'
# tperiod = '1972-2013'
simu = 'ssp585'
tperiod = '2015-2099'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
# Open the pickle file in binary read mode
with open(savefile, 'rb') as file:
# Load the dictionary from the pickle file
data2plot = pickle.load(file)
#
#--------------------
# Figure parameters
#--------------------
#____________________
# figname
figname = savefile.replace('data2plot', 'scatter_plot').replace('.pckl', '.png')
#____________________
# list
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['atlantic light', 'atlantic dense', 'southern light', 'southern dense']
#____________________
# Figure
fwidth_cm, fheight_cm = 17, 11.3333
nrow, ncol = 2, 3
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#____________________
# labels
region_label_dict = {
'southern dense': 'Southern\ndense waters',
'southern light': 'Southern\nlight waters',
'atlantic dense': 'Atlantic\ndense waters',
'atlantic light': 'Atlantic\nlight waters',
}
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
#____________________
# Colors and maps
a = cmcrameri.cm.lajolla
colors = a(np.linspace(0,1,250))
region_color_dict = {
'southern dense': colors[50],
'southern light': colors[50*2],
'atlantic dense': colors[50*3],
'atlantic light': colors[50*4]
}
#______________
# cmap
a = cmcrameri.cm.devon
colors = a(np.linspace(0,1,200))
newcmap = ListedColormap(colors[50:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[49])
newcmap.set_bad("none")
# a = cm.Wistia
# colors = a(np.linspace(0,1,300))
# newcmap = ListedColormap(colors[30:-1])
# newcmap.set_over(colors[-1])
# newcmap.set_under(colors[29])
# newcmap.set_bad("none")
#____________________
# keywords args
kwpcm = {
'southern and atlantic': dict(cmap = newcmap, norm=mpl.colors.LogNorm(vmin=1, vmax=1000)),
'total': dict(cmap=ListedColormap(['#D3D3D3']), norm=mpl.colors.LogNorm(vmin=1, vmax=1000))
}
xlim = (-10, 10)
ylim = ( -3, 3)
#--------------------
# Plot
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
zax = ax.flat[iesm]
zax.axhline(0, lw=.5, c='k')
zax.axvline(0, lw=.5, c='k')
ttl = alphabet_reverse.pop()+') '+esm_name_dict[vesm]
zax.set_title(ttl, loc='left')
zax.set_xlim(xlim)
zax.set_ylim(ylim)
#____________________
# Full scatter plot in background
sel_scatter = 'total'
xxx = data2plot[vesm][sel_scatter]['histogram2d']['xxx']
yyy = data2plot[vesm][sel_scatter]['histogram2d']['yyy']
ccc = data2plot[vesm][sel_scatter]['histogram2d']['ccc']
zax.pcolormesh(xxx, yyy, ccc, **kwpcm[sel_scatter])
#____________________
# Selected scatter plot
sel_scatter = 'southern and atlantic'
xxx = data2plot[vesm][sel_scatter]['histogram2d']['xxx']
yyy = data2plot[vesm][sel_scatter]['histogram2d']['yyy']
ccc = data2plot[vesm][sel_scatter]['histogram2d']['ccc']
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm[sel_scatter])
pcm.flat[iesm] = zwpcm
#____________________
# Add regression lines
lines = []
tab_row1 = []
tab_row2 = []
tab_row3 = []
for region in region_list:
xxx = data2plot[vesm][region]['linregress']['sampled_X']
yyy = data2plot[vesm][region]['linregress']['sampled_Y_predicted']
yyymin = data2plot[vesm][region]['linregress']['lower_band']
yyymax = data2plot[vesm][region]['linregress']['upper_band']
# yyymin = data2plot[vesm][region]['linregress']['lower_pred_band']
# yyymax = data2plot[vesm][region]['linregress']['upper_pred_band']
zax.fill_between(xxx, yyymin, yyymax, color=region_color_dict[region], alpha=0.5, lw=0)
ll, = zax.plot(xxx, yyy, label=region_label_dict[region], color=region_color_dict[region], lw=1)
lines.append(ll)
#____________________
# Save some notes for table
slope = data2plot[vesm][region]['linregress']['slope']
rvalue = data2plot[vesm][region]['linregress']['rvalue']
# ocean_frac = data2plot[vesm][region]['volume']/data2plot[vesm]['total']['volume']*100
ocean_frac = data2plot[vesm][region]['volume']/data2plot[vesm]['deep']['volume']*100
tab_row1.append(f'{np.floor(rvalue**2*100)/100:.2f}')
tab_row2.append(f'{ocean_frac:.0f}%')
tab_row3.append(f'{slope:.2f}')
#
#____________________
# add notes
kwtext = dict(ha='center', va='center_baseline', fontsize=6)
# kwtext = dict(ha='center', va='top', fontsize=6)
ytxt = .35 #.97
xtxt1 = .5+.09
xtxt2 = xtxt1+.15
xtxt3 = xtxt2+.15
zax.text(xtxt1, ytxt, r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$', transform=zax.transAxes, color='k', **kwtext)
# zax.text(xtxt1, ytxt, r'Slope', transform=zax.transAxes, color='k', **kwtext)
zax.text(xtxt2, ytxt, '$R^2$' , transform=zax.transAxes, color='k', **kwtext)
zax.text(xtxt3, ytxt, 'Ocean\nfraction', transform=zax.transAxes, color='k', **kwtext)
ytxt-=.1
for iregion, vregion in enumerate(region_list):
zax.text(xtxt2, ytxt, tab_row1[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
zax.text(xtxt3, ytxt, tab_row2[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
zax.text(xtxt1, ytxt, tab_row3[iregion], transform=zax.transAxes, color=region_color_dict[vregion], **kwtext)
ytxt-=.07
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
ax.flat[-1].axis('off')
#____________________
# legend
handles = [plt.Line2D([0], [0], marker='None', linestyle='-', label=region_label_dict[region], color=region_color_dict[region], lw=2) for region in region_list]
zax = ax.flat[-1]
zax.legend(handles=handles, handlelength=0.5, frameon=False, handletextpad=1, loc='upper right', bbox_to_anchor=(1, 1))
#____________________
# axis labels
for zax in ax[: , 0]: zax.set_ylabel(f'AOU trends, {tperiod}\n'+r'[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]')
for zax in ax[-1, :]: zax.set_xlabel(f'Age trends, {tperiod} '+r'[yr yr$^{-1}$]')
ax[0, -1].set_xlabel(f'Age trends, {tperiod} '+r'[yr yr$^{-1}$]')
#---------------------
# Colorbar
#---------------------
zw1 = ax.flat[-2].get_position()
nx0 = zw1.x1 + .2*zw1.width
ny0 = zw1.y0
nh = zw1.height
nw = 0.03*nh
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='vertical', \
ticklocation='right', extend='max')
cbar.set_label(r'Number of data point')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Plot water-masses mask¶
In [18]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Scatter plot ∆AOU vs ∆age and linear regression')
print('## Plot water-masses mask')
#____________________
# Load data2plot
# simu = 'historical'
# tperiod = '1972-2013'
simu = 'ssp585'
tperiod = '2015-2099'
# savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
# Open the pickle file in binary read mode
with open(savefile, 'rb') as file:
# Load the dictionary from the pickle file
data2plot = pickle.load(file)
#
figname = savefile.replace('data2plot', 'section_water_mass_distribution_zonal_average').replace('.pckl', '.png')
#--------------------
# Figure parameters
#--------------------
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['southern light', 'southern dense', 'atlantic light', 'atlantic dense']
#______________
# Figure size
nrow, ncol = len(esm_list), len(region_list)
fwidth_cm, fheight_cm = 4*ncol, 3*nrow
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
#______________
# cmap
a = cmcrameri.cm.batlow_r
colors = a(np.linspace(0,1,10))
newcmap = ListedColormap(colors[:])
newcmap.set_over('lightgrey')
newcmap.set_under('lightgrey')
newcmap.set_bad("none")
#______________
# pcm keywords
kwpcm = dict(cmap=newcmap, vmin=0, vmax=1)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
region_label_dict = {
'total': 'Total ocean',
'deep' : 'Deep ocean',
'deep significant' : 'Deep ocean\nsignificant',
'southern' : 'Southern waters',
'southern dense': 'Southern\ndense waters',
'southern light': 'Southern\nlight waters',
'atlantic': 'Atlantic waters',
'atlantic dense': 'Atlantic\ndense waters',
'atlantic light': 'Atlantic\nlight waters',
'southern and atlantic': 'Southern and\nAtlantic waters',
'reminder deep and significant': 'Rest of deep\nocean significant',
'deep and non significant': 'Deep ocean\nnon-significant',
'surface': 'Upper ocean'
}
#______________
# Create figure and axes
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#--------------------
# Plot
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iregion, vregion in enumerate(region_list):
zax = ax[iesm, iregion]
xxx = data2plot[vesm][vregion]['water mass distribution']['zonal average']['xxx']
yyy = data2plot[vesm][vregion]['water mass distribution']['zonal average']['yyy']
ccc = data2plot[vesm][vregion]['water mass distribution']['zonal average']['ccc']
ccc = np.where(ccc>1, 1, ccc) # small fix for some values that are above 1
ccc = np.where(ccc==0, -1, ccc) # small fix for having values equal to zero set negative for visualisation
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iregion] = zwpcm
zax.set_ylim((6000, -10))
zax.set_xlim((-90, 90))
# ocean_frac = data2plot[vesm][vregion]['volume']/data2plot[vesm]['total']['volume']*100
# ttl = extended_alphabet_reverse.pop() + f') {ocean_frac:.0f}% of ocean'
# zax.text(-80, 800, ttl)
ocean_frac = data2plot[vesm][vregion]['volume']/data2plot[vesm]['deep']['volume']*100
ttl = extended_alphabet_reverse.pop() + f') {ocean_frac:.0f}% of deep ocean'
zax.text(-80, 800, ttl, fontsize=6)
# zax.set_title(ttl, loc='left')
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.1, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for iregion, vregion in enumerate(region_list) :
zax = ax[0, iregion]
zax.text(.5, 1.2, region_label_dict[vregion], transform=zax.transAxes, ha='center', va='bottom')
#
for zax in ax[:, 0]: zax.set_ylabel('Depth [m]')
for zax in ax[-1, :]: zax.set_xlabel('Latitude [Deg. N]')
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, 1].get_position()
nx0 = zw1.x0
ny0 = zw1.y0 - .8*zw1.height
nw = zw2.x1 - nx0
nh = 0.06*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
cbar.set_label(r'Zonal fraction of grid cell')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Constraint plot¶
linreg(∆AOU vs ∆age) in SSP585 vs linreg(∆AOU vs ∆age) in historical¶
In [34]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Constrain plot')
print('## linreg(∆AOU vs ∆age) in SSP585 vs linreg(∆AOU vs ∆age) in historical')
#----------------
# Del some existing variables
#----------------
print('Delete some existing variables...')
def check_and_delete_variable(variable_name):
if variable_name in globals():
del globals()[variable_name]
print(f"{variable_name} deleted")
elif variable_name in locals():
del locals()[variable_name]
print( f"{variable_name} deleted" )
else:
print( f"{variable_name} does not exist")
#
for vvv in ['data2plot']: check_and_delete_variable(vvv)
gc.collect()
#____________________
# figname
figname = dirout + f'constraint_plot_linreg∆AOUvs∆age_in_SSP585_vs_linreg∆AOUvs∆age_in_historical.png'
#--------------------
# Load data2plot
#--------------------
data2plot = {}
simu, tperiod = 'historical', '1972-2013'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2plot['XXX'] = pickle.load(file)
#
# data2plot['XXX']['title'] = r'Sensitivity of trends in AOU to trends in age [mmol.m$^{-3}$.yr$^{-1}$]'+f'\n{tperiod}, historical simulation'
data2plot['XXX']['title'] = r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$'+\
f' on years {tperiod}\n[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
simu, tperiod = 'ssp585', '2015-2099'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2plot['YYY'] = pickle.load(file)
#
# data2plot['YYY']['title'] = r'Sensitivity of trends in AOU to trends in age [mmol.m$^{-3}$.yr$^{-1}$]'+f'\n{tperiod}, SSP5-8.5 simulation'
data2plot['YYY']['title'] = r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$'+\
f' on years {tperiod}\n[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
# suptitle = r'OUR [mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
#--------------------
# Figure param
#--------------------
#____________________
# list
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L']
region_list = ['southern light', 'atlantic light', 'southern dense', 'atlantic dense']
#____________________
# Create figure
fwidth_cm, fheight_cm = 12, 12
nrow, ncol = 2, 2
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
region_label_dict = {
'southern dense': 'Southern dense waters',
'southern light': 'Southern light waters',
'atlantic dense': 'Atlantic dense waters',
'atlantic light': 'Atlantic light waters',
}
#--------------------
# Plot
#--------------------
len_esm = 0
for vesm in esm_list: len_esm=np.max([len_esm, len(vesm)])
spaces = ' '*len_esm
for iregion, vregion in enumerate(region_list):
print(f'\n----- {vregion} -----\n')
print(spaces+' contemporary future')
zax = ax.flat[iregion]
xxx_save, yyy_save = [], []
if iregion==0: model_handles = []
for iesm, vesm in enumerate(esm_list):
tvalue = data2plot['XXX'][vesm][vregion]['linregress']['tvalue']
xxx = data2plot['XXX'][vesm][vregion]['linregress']['slope']
xerr = tvalue * data2plot['XXX'][vesm][vregion]['linregress']['stderr']
xrval = data2plot['XXX'][vesm][vregion]['linregress']['rvalue']
tvalue = data2plot['YYY'][vesm][vregion]['linregress']['tvalue']
yyy = data2plot['YYY'][vesm][vregion]['linregress']['slope']
yerr = tvalue * data2plot['YYY'][vesm][vregion]['linregress']['stderr']
yrval = data2plot['YYY'][vesm][vregion]['linregress']['rvalue']
zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='o', markersize=4, color=esm_color_dict[vesm])
if xrval**2 < 0.7: zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='_', markersize=8, color='r')
if yrval**2 < 0.7: zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='|', markersize=8, color='r')
xxx_save.append(xxx)
yyy_save.append(yyy)
if iregion==0: model_handles.append(plt.Line2D([0], [0], marker='o', markersize=4,
color=esm_color_dict[vesm], linestyle='None', label=esm_name_dict[vesm]))
extraspaces = ' '*(len(spaces) - len(vesm))
print(f'{vesm}: '+extraspaces+f'{xxx:.2f}±{xerr:.2f} {yyy:.2f}±{yerr:.2f}')
#
#______________
# Add regression line
# Combine `xxx` and `yyy` and sort based on `xxx`
xxx_yyy = sorted(zip(xxx_save, yyy_save))
# Unzip the sorted pairs
sorted_xxx, sorted_yyy = zip(*xxx_yyy)
xxx_save = np.array(sorted_xxx)
yyy_save = np.array(sorted_yyy)
results = stats.linregress(xxx_save, yyy_save)
# results.slope
# results.intercept
# results.rvalue
# results.pvalue
# results.stderr
# results.intercept_stderr
predicted = results.intercept + results.slope * xxx_save
n = len(xxx_save) # Number of observations
t_value = stats.t.ppf(1 - 0.025, n - 2) # t-value for 95% confidence interval with n-2 degrees of freedom
# Standard error of the predicted y-values (y_hat)
s_err = np.sqrt(np.sum((yyy_save - predicted) ** 2) / (n - 2))
conf_interval = t_value * s_err * np.sqrt(1 / n + (xxx_save - np.mean(xxx_save))**2 / np.sum((xxx_save - np.mean(xxx_save))**2))
lower_band = predicted - conf_interval
upper_band = predicted + conf_interval
zax.fill_between(xxx_save, lower_band, upper_band, color='k', alpha=0.2, lw=0)
zax.plot(xxx_save, predicted, color='k', lw=.5)
# R2 = f'$R^2={np.floor(results.rvalue**2*100)/100:.2f}'
R2 = rf'$R^2$={np.round(results.rvalue**2, decimals=2)}'
pvalue = rf'p-value={np.round(results.pvalue, decimals=2):.2f}' if results.pvalue >= .01 else rf'p-value<0.01'
zwtxt = R2+'\n'+pvalue
#zwtxt = f"$R^2={R2}$\np-value: {results.pvalue:.2f}"
zax.text(.05, .95, zwtxt, transform=zax.transAxes, ha='left', va='top', size=6, \
bbox=dict(facecolor='white', edgecolor='black', linewidth=0.5, boxstyle='square,pad=0.5'))
#______________
# Decorate
# zax.set_xlabel(f'{data2plot["XXX"]["title"]}')
# zax.set_ylabel(f'{data2plot["YYY"]["title"]}')
# zax.set_xlabel(f'Sensitivity of ∆AOU to ∆age\n{data2plot["XXX"]["tperiod"]}')
# zax.set_ylabel(f'Sensitivity of ∆AOU to ∆age\n{data2plot["YYY"]["tperiod"]}')
grid_color = '#b0b0b0'
grid_linestyle = '--'
grid_linewidth = .5
zax.grid(True, which='major', axis='both', color=grid_color, linestyle=grid_linestyle, linewidth=grid_linewidth)
ttl = alphabet_reverse.pop()+') '+region_label_dict[vregion]
zax.set_title(ttl, loc='left')
# Make same x and y range
xlim0 = zax.get_xlim()
ylim0 = zax.get_ylim()
xylim = (np.min([xlim0, ylim0]), np.max([xlim0, ylim0]))
zax.set_xlim(xylim)
zax.set_ylim(xylim)
# 1:1 line
xxx = np.linspace(zax.get_xlim()[0], zax.get_xlim()[1], 10)
zax.plot(xxx, xxx, c=grid_color, ls=grid_linestyle, lw=grid_linewidth)
xlim = zax.get_xlim()
ylim = zax.get_ylim()
zax.set_aspect(abs( (xlim[1]- xlim[0])/(ylim[1]- ylim[0]) ))
#______________
# Add results from observations
if vregion in ['southern dense', 'atlantic dense']:
if vregion in ['southern dense']:
nnn=23
tval = stats.t.ppf(1 - 0.025, nnn - 2)
x_line, x_uncertainty = 0.04, 0.0029*tval
elif vregion in ['atlantic dense']:
nnn=20
tval = stats.t.ppf(1 - 0.025, nnn - 2)
x_line, x_uncertainty = 0.11, 0.0073*tval
#
zax.fill_betweenx(ylim, x1=x_line-x_uncertainty, x2=x_line+x_uncertainty, color='tab:blue', alpha=0.2, lw=0)
zax.axvline(x=x_line, color='tab:blue', lw=.5)
print(f'Observations: '+f'{x_line:.2f}±{x_uncertainty:.2f}')
#
if vregion == 'southern dense':
zax.text(x_line, 0.025, 'Observation', ha='left', va='bottom', size=6, color='tab:blue')
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
for zax in ax[:, 0]:
zax.set_ylabel(f'{data2plot["YYY"]["title"]}')
#
for zax in ax[-1, :]:
zax.set_xlabel(f'{data2plot["XXX"]["title"]}')
#
#__________________
# Add legend
ax.flat[0].legend(handles=model_handles, handlelength=0.5, frameon=False, borderaxespad=0, borderpad=0,
handletextpad=.5, loc='lower right', bbox_to_anchor=(1, 0.03))
#__________________
# Suptile
# fig.suptitle(suptitle, x=0.5, y=1, ha='center', va='bottom')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
linreg(∆AOU vs ∆age) in SSP585 vs linreg(∆AOU vs ∆age) in historical with estimations from different time periods¶
In [9]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Constrain plot')
print('## linreg(∆AOU vs ∆age) in SSP585 vs linreg(∆AOU vs ∆age) in historical with estimations from different time periods')
#----------------
# Del some existing variables
#----------------
print('Delete some existing variables...')
def check_and_delete_variable(variable_name):
if variable_name in globals():
del globals()[variable_name]
print(f"{variable_name} deleted")
elif variable_name in locals():
del locals()[variable_name]
print( f"{variable_name} deleted" )
else:
print( f"{variable_name} does not exist")
#
for vvv in ['data2plot']: check_and_delete_variable(vvv)
gc.collect()
#____________________
# figname
figname = dirout + f'constraint_plot_linreg∆AOUvs∆age_in_SSP585_vs_linreg∆AOUvs∆age_in_historical_with_estimation_from_different_periods.png'
#--------------------
# Load data2plot
#--------------------
data2plot = {}
simu, tperiod = 'historical', '1972-2013'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2plot['XXX'] = pickle.load(file)
#
# data2plot['XXX']['title'] = r'Sensitivity of trends in AOU to trends in age [mmol.m$^{-3}$.yr$^{-1}$]'+f'\n{tperiod}, historical simulation'
data2plot['XXX']['title'] = r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$'+\
f' on years {tperiod}\n[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
simu, tperiod = 'ssp585', '2015-2099'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2plot['YYY'] = pickle.load(file)
#
# data2plot['YYY']['title'] = r'Sensitivity of trends in AOU to trends in age [mmol.m$^{-3}$.yr$^{-1}$]'+f'\n{tperiod}, SSP5-8.5 simulation'
data2plot['YYY']['title'] = r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$'+\
f' on years {tperiod}\n[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
# suptitle = r'OUR [mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
#______________
# Load estimates from different time periods
# define period_list
nyear = 50
yearsend = np.arange(2099, 1850, -nyear)
yearsstart = np.arange(2099-(nyear-1), 1850-(nyear-1), -nyear)
period_list = []
for yend, ystart in zip(yearsend, yearsstart):
period_list.append((ystart, yend))
#
period_list.reverse()
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
region_list = ['southern light', 'atlantic light', 'southern dense', 'atlantic dense']
data2plot['XXX_bis'] = {'tperiod':[]}
data2plot['YYY_bis'] = {'tperiod':[]}
for iesm, vesm in enumerate(esm_list):
data2plot['XXX_bis'][vesm] = {}
data2plot['YYY_bis'][vesm] = {}
for watermass in region_list:
data2plot['XXX_bis'][vesm][watermass] = []
data2plot['YYY_bis'][vesm][watermass] = []
#
#
for iperiod, vperiod in enumerate(period_list[1:]):
tperiod = f'{vperiod[0]}-{vperiod[1]}'
if vperiod[-1]<2000: data2plot['XXX_bis']['tperiod'].append(tperiod)
elif vperiod[-1]>=2000: data2plot['YYY_bis']['tperiod'].append(tperiod)
for iesm, vesm in enumerate(esm_list):
savefile = '/mnt/reef-ns1002k/daco/MY_JUPYTER_NOTEBOOKS/OceanICU/25-05-05-daou-vs-dage-for-different-time-period-v2/'\
+ f'{vesm}_distribution_and_linear_regression_of_aou_trend_vs_age_trend_{tperiod}.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
for watermass in region_list:
zwour = data2process_significant[watermass]['linregress']['slope']
if vperiod[-1]<2000: data2plot['XXX_bis'][vesm][watermass].append(zwour)
elif vperiod[-1]>=2000: data2plot['YYY_bis'][vesm][watermass].append(zwour)
#
#
#
In [21]:
#--------------------
# Figure param
#--------------------
#____________________
# list
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L']
region_list = ['southern light', 'atlantic light', 'southern dense', 'atlantic dense']
#____________________
# Create figure
fwidth_cm, fheight_cm = 12, 12
nrow, ncol = 2, 2
fsize = (fwidth_cm*cm2in, fheight_cm*cm2in) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, squeeze=False)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
region_label_dict = {
'southern dense': 'Southern dense waters',
'southern light': 'Southern light waters',
'atlantic dense': 'Atlantic dense waters',
'atlantic light': 'Atlantic light waters',
}
#--------------------
# Plot
#--------------------
len_esm = 0
for vesm in esm_list: len_esm=np.max([len_esm, len(vesm)])
spaces = ' '*len_esm
for iregion, vregion in enumerate(region_list):
print(f'\n----- {vregion} -----\n')
print(spaces+' contemporary future')
zax = ax.flat[iregion]
xxx_save, yyy_save = [], []
if iregion==0: model_handles = []
for iesm, vesm in enumerate(esm_list):
tvalue = data2plot['XXX'][vesm][vregion]['linregress']['tvalue']
xxx = data2plot['XXX'][vesm][vregion]['linregress']['slope']
xerr = tvalue * data2plot['XXX'][vesm][vregion]['linregress']['stderr']
xrval = data2plot['XXX'][vesm][vregion]['linregress']['rvalue']
tvalue = data2plot['YYY'][vesm][vregion]['linregress']['tvalue']
yyy = data2plot['YYY'][vesm][vregion]['linregress']['slope']
yerr = tvalue * data2plot['YYY'][vesm][vregion]['linregress']['stderr']
yrval = data2plot['YYY'][vesm][vregion]['linregress']['rvalue']
zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='o', markersize=4, \
markeredgecolor='k', markeredgewidth=0.5, color=esm_color_dict[vesm])
xxx_save.append(xxx)
yyy_save.append(yyy)
if iregion==0: model_handles.append(plt.Line2D([0], [0], marker='o', markersize=4, markeredgecolor='k', markeredgewidth=0.5,
color=esm_color_dict[vesm], linestyle='None', label=esm_name_dict[vesm]))
extraspaces = ' '*(len(spaces) - len(vesm))
print(f'{vesm}: '+extraspaces+f'{xxx:.2f}±{xerr:.2f} {yyy:.2f}±{yerr:.2f}')
# Add estimates from different time periods
xxx_bis = data2plot['XXX_bis'][vesm][vregion]
yyy_bis = data2plot['YYY_bis'][vesm][vregion]
inum = 1
for vx in xxx_bis:
zax.text(vx, yyy, str(inum), fontsize=5, ha='center', va='center', color=esm_color_dict[vesm])
inum+=1
for vy in yyy_bis:
zax.text(xxx, vy, str(inum), fontsize=5, ha='center', va='center', color=esm_color_dict[vesm])
inum+=1
zax.errorbar(xxx*np.ones_like(yyy_bis), yyy_bis, fmt='+', markersize=3, markeredgewidth=0, color=esm_color_dict[vesm])
zax.errorbar(xxx_bis, yyy*np.ones_like(xxx_bis), fmt='+', markersize=3, markeredgewidth=0, color=esm_color_dict[vesm])
# zax.errorbar(xxx*np.ones_like(yyy_bis), yyy_bis, fmt='+', markersize=3, markeredgewidth=1, color=esm_color_dict[vesm])
# zax.errorbar(xxx_bis, yyy*np.ones_like(xxx_bis), fmt='+', markersize=3, markeredgewidth=1, color=esm_color_dict[vesm])
if xrval**2 < 0.7: zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='_', markersize=8, markeredgewidth=0.5, color='r')
if yrval**2 < 0.7: zax.errorbar(xxx, yyy, xerr=xerr, yerr=yerr, fmt='|', markersize=8, markeredgewidth=0.5, color='r')
#
#______________
# Add regression line
# Combine `xxx` and `yyy` and sort based on `xxx`
xxx_yyy = sorted(zip(xxx_save, yyy_save))
# Unzip the sorted pairs
sorted_xxx, sorted_yyy = zip(*xxx_yyy)
xxx_save = np.array(sorted_xxx)
yyy_save = np.array(sorted_yyy)
results = stats.linregress(xxx_save, yyy_save)
# results.slope
# results.intercept
# results.rvalue
# results.pvalue
# results.stderr
# results.intercept_stderr
predicted = results.intercept + results.slope * xxx_save
n = len(xxx_save) # Number of observations
t_value = stats.t.ppf(1 - 0.025, n - 2) # t-value for 95% confidence interval with n-2 degrees of freedom
# Standard error of the predicted y-values (y_hat)
s_err = np.sqrt(np.sum((yyy_save - predicted) ** 2) / (n - 2))
conf_interval = t_value * s_err * np.sqrt(1 / n + (xxx_save - np.mean(xxx_save))**2 / np.sum((xxx_save - np.mean(xxx_save))**2))
lower_band = predicted - conf_interval
upper_band = predicted + conf_interval
zax.fill_between(xxx_save, lower_band, upper_band, color='k', alpha=0.2, lw=0)
zax.plot(xxx_save, predicted, color='k', lw=.5)
# R2 = f'$R^2={np.floor(results.rvalue**2*100)/100:.2f}'
R2 = rf'$R^2$={np.round(results.rvalue**2, decimals=2)}'
pvalue = rf'p-value={np.round(results.pvalue, decimals=2):.2f}' if results.pvalue >= .01 else rf'p-value<0.01'
zwtxt = R2+'\n'+pvalue
#zwtxt = f"$R^2={R2}$\np-value: {results.pvalue:.2f}"
zax.text(.05, .95, zwtxt, transform=zax.transAxes, ha='left', va='top', size=6, \
bbox=dict(facecolor='white', edgecolor='black', linewidth=0.5, boxstyle='square,pad=0.5'))
#______________
# Decorate
# zax.set_xlabel(f'{data2plot["XXX"]["title"]}')
# zax.set_ylabel(f'{data2plot["YYY"]["title"]}')
# zax.set_xlabel(f'Sensitivity of ∆AOU to ∆age\n{data2plot["XXX"]["tperiod"]}')
# zax.set_ylabel(f'Sensitivity of ∆AOU to ∆age\n{data2plot["YYY"]["tperiod"]}')
grid_color = '#b0b0b0'
grid_linestyle = '--'
grid_linewidth = .5
zax.grid(True, which='major', axis='both', color=grid_color, linestyle=grid_linestyle, linewidth=grid_linewidth)
ttl = alphabet_reverse.pop()+') '+region_label_dict[vregion]
zax.set_title(ttl, loc='left')
# Make same x and y range
xlim0 = zax.get_xlim()
ylim0 = zax.get_ylim()
xylim = (np.min([xlim0, ylim0]), np.max([xlim0, ylim0]))
zax.set_xlim(xylim)
zax.set_ylim(xylim)
# 1:1 line
xxx = np.linspace(zax.get_xlim()[0], zax.get_xlim()[1], 10)
zax.plot(xxx, xxx, c=grid_color, ls=grid_linestyle, lw=grid_linewidth)
xlim = zax.get_xlim()
ylim = zax.get_ylim()
zax.set_aspect(abs( (xlim[1]- xlim[0])/(ylim[1]- ylim[0]) ))
#______________
# Add results from observations
if vregion in ['southern dense', 'atlantic dense']:
if vregion in ['southern dense']:
nnn=23
tval = stats.t.ppf(1 - 0.025, nnn - 2)
x_line, x_uncertainty = 0.04, 0.0029*tval
elif vregion in ['atlantic dense']:
nnn=20
tval = stats.t.ppf(1 - 0.025, nnn - 2)
x_line, x_uncertainty = 0.11, 0.0073*tval
#
zax.fill_betweenx(ylim, x1=x_line-x_uncertainty, x2=x_line+x_uncertainty, color='tab:blue', alpha=0.2, lw=0)
zax.axvline(x=x_line, color='tab:blue', lw=.5)
print(f'Observations: '+f'{x_line:.2f}±{x_uncertainty:.2f}')
#
if vregion == 'southern dense':
zax.text(x_line, 0.025, 'Observation', ha='left', va='bottom', size=6, color='tab:blue')
#
#
fig.tight_layout()
#--------------------
# Decorate
#--------------------
for zax in ax[:, 0]:
zax.set_ylabel(f'{data2plot["YYY"]["title"]}')
#
for zax in ax[-1, :]:
zax.set_xlabel(f'{data2plot["XXX"]["title"]}')
#
#__________________
# Add legend
ax.flat[0].legend(handles=model_handles, handlelength=0.5, frameon=False, borderaxespad=0, borderpad=0,
handletextpad=.5, loc='lower right', bbox_to_anchor=(1, 0.03))
#__________________
# Suptile
# fig.suptitle(suptitle, x=0.5, y=1, ha='center', va='bottom')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Figure ∆AOU = OUR * ∆Age + reminder¶
Prepare data2plot¶
In [9]:
%%time
%%memit -c
# ca. 2min
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
tperiod = '2015-2099'
#----------------
# Define some functions
#----------------
def compute_xavg(zwnp, zwvolume):
zwda = xr.DataArray(zwnp, dims=zwvolume.dims, coords=zwvolume.coords)
zwda_xavg = zonal_avg(zwda, zwvolume)
xavg_dict = {'xxx': zwda_xavg.latitude.values, 'yyy': zwda_xavg.depth.values, 'ccc': zwda_xavg.values}
return xavg_dict
#
#----------------
# Initiate data2plot
#----------------
data2plot = {
'ESM': [], \
'Global': {
'∆AOU': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'reminder': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'R2': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'CI': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'volume': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}
}, \
'xavg': {
'∆AOU': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'reminder': {'southern and atlantic significant': [], \
'southern and atlantic': []}
}
}
#----------------
# Load data
#----------------
print('Load data...')
if tperiod=='2015-2099': simu = 'ssp585'
elif tperiod=='1972-2013': simu = 'historical'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, 'rb') as file:
data2process_with_low_significant = pickle.load(file)
#
#----------------
# Loop on ESM
#----------------
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot['ESM'].append(vesm)
#----------------
# Compute volume and omask
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
fname = netcdfdir+f'{vesm}_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_sigma0 = zwds['sigma0']
if vesm == 'MPI-ESM1-2-LR' :
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask').drop(['latitude', 'longitude'])
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zwda_sigma0, zarea, omask
gc.collect()
#----------------
# Southern and atlantic significant
#----------------
print(' Southern and atlantic significant...')
zw_delta_aou_SA, zw_delta_age_SA, zw_reminder_SA = 0, 0, 0
zwvolume_SA, zwr2_SA = 0, 0
zwCI_SA = 0
zw_delta_aou_3D_SA, zw_delta_age_3D_SA, zw_reminder_3D_SA = [], [], []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
#______________
# Global
zwaou = data2process_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_significant[vesm][watermass]['integrated age trend']
zwour = data2process_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_significant[vesm][watermass]['volume']
zw_delta_age = zwour * zwage
zw_reminder = zwaou - zw_delta_age
zw_delta_aou_SA += zwaou
zw_delta_age_SA += zw_delta_age
zw_reminder_SA += zw_reminder
zwr2_SA += zwr2 * zwvolume
zwvolume_SA += zwvolume
#______________
# XAVG
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_delta_aou_3D_SA.append(zw_delta_aou_3D)
zw_delta_age_3D_SA.append(zw_delta_age_3D)
zw_reminder_3D_SA .append(zw_reminder_3D )
#______________
# Confidence interval of aou prediction
zwCI_3D = data2process_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zwCI_SA += np.nansum(zwCI_3D*(volume.values))
#
zwr2_SA = zwr2_SA / zwvolume_SA
zw_delta_aou_3D_SA = np.array(zw_delta_aou_3D_SA)
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_reminder_3D_SA = np.array(zw_reminder_3D_SA )
all_nan_mask = np.all(np.isnan(zw_delta_aou_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_aou_3D_SA, axis=0)
zw_delta_aou_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_reminder_3D_SA), axis=0)
zwnansum = np.nansum(zw_reminder_3D_SA, axis=0)
zw_reminder_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic significant'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou_SA)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder_SA)
data2plot[datatype]['R2' ][watermass].append(zwr2_SA)
data2plot[datatype]['CI' ][watermass].append(zwCI_SA)
data2plot[datatype]['volume' ][watermass].append(zwvolume_SA)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D_SA, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D_SA, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
#----------------
# Southern and atlantic including low significant water-masses
#----------------
print(' Southern and atlantic including low significant water-masses...')
zw_delta_aou_SA, zw_delta_age_SA, zw_reminder_SA = 0, 0, 0
zwvolume_SA, zwr2_SA = 0, 0
zwCI_SA = 0
zw_delta_aou_3D_SA, zw_delta_age_3D_SA, zw_reminder_3D_SA = [], [], []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
#______________
# Global
zwaou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_with_low_significant[vesm][watermass]['integrated age trend']
zwour = data2process_with_low_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_with_low_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_with_low_significant[vesm][watermass]['volume']
zw_delta_age = zwour * zwage
zw_reminder = zwaou - zw_delta_age
zw_delta_aou_SA += zwaou
zw_delta_age_SA += zw_delta_age
zw_reminder_SA += zw_reminder
zwr2_SA += zwr2 * zwvolume
zwvolume_SA += zwvolume
#______________
# XAVG
zw_pred_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_with_low_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_delta_aou_3D_SA.append(zw_delta_aou_3D)
zw_delta_age_3D_SA.append(zw_delta_age_3D)
zw_reminder_3D_SA .append(zw_reminder_3D )
#______________
# Confidence interval of aou prediction
zwCI_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zwCI_SA += np.nansum(zwCI_3D*(volume.values))
#
zwr2_SA = zwr2_SA / zwvolume_SA
zw_delta_aou_3D_SA = np.array(zw_delta_aou_3D_SA)
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_reminder_3D_SA = np.array(zw_reminder_3D_SA )
all_nan_mask = np.all(np.isnan(zw_delta_aou_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_aou_3D_SA, axis=0)
zw_delta_aou_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_reminder_3D_SA), axis=0)
zwnansum = np.nansum(zw_reminder_3D_SA, axis=0)
zw_reminder_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou_SA)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder_SA)
data2plot[datatype]['R2' ][watermass].append(zwr2_SA)
data2plot[datatype]['CI' ][watermass].append(zwCI_SA)
data2plot[datatype]['volume' ][watermass].append(zwvolume_SA)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D_SA, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D_SA, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
#----------------
# Total, deep and deep significant (only ∆AOU and volume)...
#----------------
print(' Total, deep and deep significant (only ∆AOU and volume)...')
datatype = 'Global'
# total
watermass = 'total'
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass].append(zwvolume)
# deep significant
watermass = 'deep'
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass+' significant'].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass+' significant'].append(zwvolume)
# deep including low significance
watermass = 'deep'
zw_delta_aou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass].append(zwvolume)
#
#
#----------------
# Save data2plot in pickle
#----------------
print('Save file...')
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot¶
In [35]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## Plot')
print('################################')
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder.pckl'
with open(savefile, 'rb') as file:
data2plot = pickle.load(file)
#
watermass='southern and atlantic significant'
#____________________
# Set figname for saving figure
figname = dirout + 'plot_AOUtrend_OURxAgetrend_reminder.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot['ESM']
section_list = ['∆AOU', '∆Age', 'reminder']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B+\varepsilon$'
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'reminder' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-.2, vmax=.2)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 3
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iii, vvv in enumerate(section_list):
zwdata = data2plot['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] * zfact_section
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = '[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
error_values = np.array(data2plot['Global']['CI'][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
# bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, yerr=error_values, ecolor='white', **kwbar1)
#_________________
# Reminder
iii = 1
var = 'reminder'
bbb += hhh
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
xxx2 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx2, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.30*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add R2 text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.005*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot['Global']['R2'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val:.2f}', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'$\overline{R^2}$:', ha='left', va='bottom', size=6)
#--------------------
# Add volume text
#--------------------
iii=0
ytext = ylim[0]+0.1*(ylim[1]-ylim[0])
# totvolume = data2plot['Global']['volume']['total']
totvolume = data2plot['Global']['volume']['deep']
for ind, val in enumerate(data2plot['Global']['volume'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val/totvolume[ind]*100:.0f}%', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'Vol. ratio:', ha='left', va='bottom', size=6)
#--------------------
# Add share of dAge
#--------------------
iii=0
ytext = 0.005*(ylim[1]-ylim[0])
daou = data2plot['Global']['∆AOU'][watermass]
error_values = data2plot['Global']['CI'][watermass]
for ind, val in enumerate(data2plot['Global']['∆Age'][watermass]):
xtext = xxx1[ind]
zax.text( xtext, ytext, fr'{val/daou[ind]*100:.0f}%', \
c='white', ha='center', va='bottom', size=6)
# zax.text( xtext, ytext, fr'{val/daou[ind]*100:.0f}$\pm${np.ceil(error_values[ind]/daou[ind]*100):.0f}%', \
# c='white', ha='center', va='bottom', size=6)
#
#--------------------
# Print share of dAge
#--------------------
print('---- dAGE/dAOU ----')
daou = data2plot['Global']['∆AOU'][watermass]
dage = data2plot['Global']['∆Age'][watermass]
error_values = data2plot['Global']['CI'][watermass]
for ind, val in enumerate(data2plot['ESM']):
print(fr'{val}: {dage[ind]/daou[ind]*100:.0f}±{np.ceil(error_values[ind]/daou[ind]*100):.0f}%')
#
print('-----------------')
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAOU ----')
daou = data2plot['Global']['∆AOU'][watermass]
dage = data2plot['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {daou[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot contribution relative to ∆AOU¶
In [10]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## Plot contribution relative to ∆AOU')
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder.pckl'
with open(savefile, 'rb') as file:
data2plot = pickle.load(file)
#
watermass='southern and atlantic significant'
#____________________
# Set figname for saving figure
figname = dirout + 'plot_AOUtrend_OURxAgetrend_reminder_relative_contributions.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot['ESM']
section_list = ['∆AOU', '∆Age', 'reminder']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B$'
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'reminder' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-1.5, vmax=1.5)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 3
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
ccc_ref = data2plot['xavg']['∆AOU'][watermass][iesm]['ccc']
for iii, vvv in enumerate(section_list):
zwdata = data2plot['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] / ccc_ref
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = 'ratio to ∆AOU'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#_________________
# Reminder
iii = 1
var = 'reminder'
bbb += hhh
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx1, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.30*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add R2 text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.005*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot['Global']['R2'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val:.2f}', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'$\overline{R^2}$:', ha='left', va='bottom', size=6)
#--------------------
# Add volume text
#--------------------
iii=0
ytext = ylim[0]+0.1*(ylim[1]-ylim[0])
# totvolume = data2plot['Global']['volume']['total']
totvolume = data2plot['Global']['volume']['deep']
for ind, val in enumerate(data2plot['Global']['volume'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val/totvolume[ind]*100:.0f}%', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'Vol. ratio:', ha='left', va='bottom', size=6)
#--------------------
# Print share of dAge
#--------------------
print('---- dAGE/dAOU ----')
daou = data2plot['Global']['∆AOU'][watermass]
dage = data2plot['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot['ESM']):
print(f'{val}: {dage[ind]/daou[ind]*100:.0f}%')
#
print('-----------------')
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAOU ----')
daou = data2plot['Global']['∆AOU'][watermass]
dage = data2plot['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {daou[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Plot each watermasses¶
Prepare data2plot¶
In [11]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## Plot each watermass')
print('### Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
tperiod = '2015-2099'
#----------------
# Define some functions
#----------------
def compute_xavg(zwnp, zwvolume):
zwda = xr.DataArray(zwnp, dims=zwvolume.dims, coords=zwvolume.coords)
zwda_xavg = zonal_avg(zwda, zwvolume)
xavg_dict = {'xxx': zwda_xavg.latitude.values, 'yyy': zwda_xavg.depth.values, 'ccc': zwda_xavg.values}
return xavg_dict
#
#----------------
# Initiate data2plot
#----------------
data2plot = {
'ESM': [], \
'Global': {
'∆AOU': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'∆Age': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'reminder': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'R2': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'CI': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'volume': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}
}, \
'xavg': {
'∆AOU': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'∆Age': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'reminder': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}
}
}
#----------------
# Load data
#----------------
print(' Load data...')
if tperiod=='2015-2099': simu = 'ssp585'
elif tperiod=='1972-2013': simu = 'historical'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, 'rb') as file:
data2process_with_low_significant = pickle.load(file)
#
#----------------
# Loop on ESM
#----------------
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot['ESM'].append(vesm)
#----------------
# Compute volume and omask
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
fname = netcdfdir+f'{vesm}_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_sigma0 = zwds['sigma0']
if vesm == 'MPI-ESM1-2-LR' :
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask').drop(['latitude', 'longitude'])
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zwda_sigma0, zarea, omask
gc.collect()
#----------------
# Loop on water-masses
#----------------
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
print(f' {watermass}...')
#________________
# Significant water-masses
# Global
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_significant[vesm][watermass]['integrated age trend']
zwour = data2process_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_significant[vesm][watermass]['volume']
zwCI_3D = data2process_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zw_delta_age = zwour * zwage
zw_reminder = zw_delta_aou - zw_delta_age
zwCI = np.nansum(zwCI_3D*(volume.values))
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass+' significant'].append(zw_delta_aou)
data2plot[datatype]['∆Age' ][watermass+' significant'].append(zw_delta_age)
data2plot[datatype]['reminder' ][watermass+' significant'].append(zw_reminder)
data2plot[datatype]['R2' ][watermass+' significant'].append(zwr2)
data2plot[datatype]['CI' ][watermass+' significant'].append(zwCI)
data2plot[datatype]['volume' ][watermass+' significant'].append(zwvolume)
# xavg
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D, volume)
data2plot[datatype]['∆AOU'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_delta_age_3D, volume)
data2plot[datatype]['∆Age'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_reminder_3D, volume)
data2plot[datatype]['reminder'][watermass+' significant'].append(xavg)
#________________
# Including low significant water-masses
# Global
zw_delta_aou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_with_low_significant[vesm][watermass]['integrated age trend']
zwour = data2process_with_low_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_with_low_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_with_low_significant[vesm][watermass]['volume']
zwCI_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zw_delta_age = zwour * zwage
zw_reminder = zw_delta_aou - zw_delta_age
zwCI = np.nansum(zwCI_3D*(volume.values))
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder)
data2plot[datatype]['R2' ][watermass].append(zwr2)
data2plot[datatype]['volume' ][watermass].append(zwvolume)
data2plot[datatype]['CI' ][watermass].append(zwCI)
# xavg
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
#
#
#----------------
# Save data2plot in pickle
#----------------
print('Save file...')
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_each_watermass.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot¶
In [12]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Compute ∆AOU = OUR * ∆Age + intercept + residual')
print('## Plot each watermass')
print('### Plot')
#----------------
# Del some existing variables
#----------------
print('Delete some existing variables...')
def check_and_delete_variable(variable_name):
if variable_name in globals():
del globals()[variable_name]
print(f"{variable_name} deleted")
elif variable_name in locals():
del locals()[variable_name]
print( f"{variable_name} deleted" )
else:
print( f"{variable_name} does not exist")
#
for vvv in ['data2plot', 'data2plot1', 'data2plot2']: check_and_delete_variable(vvv)
gc.collect()
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder.pckl'
with open(savefile, 'rb') as file:
data2plot1 = pickle.load(file)
#
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_each_watermass.pckl'
with open(savefile, 'rb') as file:
data2plot2 = pickle.load(file)
#
watermass='southern light significant'
#____________________
# Set figname for saving figure
figname = dirout + f'plot_AOUtrend_OURxAgetrend_reminder_'+watermass.replace(' ', '_')+'.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot1['ESM']
section_list = ['∆AOU', '∆Age', 'reminder']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B$'
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'reminder' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-.2, vmax=.2)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 3
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iii, vvv in enumerate(section_list):
zwdata = data2plot2['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] * zfact_section
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = '[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot1['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot1['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
error_values = np.array(data2plot2['Global']['CI'][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
# bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, yerr=error_values, **kwbar1)
#_________________
# Reminder
iii = 1
var = 'reminder'
bbb += hhh
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
xxx2 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx2, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.30*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add R2 text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.005*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot2['Global']['R2'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val:.2f}', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'$\overline{R^2}$:', ha='left', va='bottom', size=6)
#--------------------
# Add volume text
#--------------------
iii=0
ytext = ylim[0]+0.1*(ylim[1]-ylim[0])
totvolume = data2plot1['Global']['volume']['total']
for ind, val in enumerate(data2plot2['Global']['volume'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val/totvolume[ind]*100:.0f}%', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'Vol. ratio:', ha='left', va='bottom', size=6)
#--------------------
# Add share of dAge
#--------------------
iii=0
ytext = 0.005*(ylim[1]-ylim[0])
daou = data2plot2['Global']['∆AOU'][watermass]
for ind, val in enumerate(data2plot2['Global']['∆Age'][watermass]):
xtext = xxx1[ind]
zax.text( xtext, ytext, f'{val/daou[ind]*100:.0f}%', c='white', ha='center', va='bottom', size=6)
#
#--------------------
# Print share of dAge
#--------------------
print('---- dAGE/dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]/daou[ind]*100:.0f}%')
#
print('-----------------')
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {daou[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
1972-2013¶
Prepare data2plot¶
In [16]:
%%time
%%memit -c
# ca. 2min
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## 1972-2013')
print('### Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
tperiod = '1972-2013'
#----------------
# Define some functions
#----------------
def compute_xavg(zwnp, zwvolume):
zwda = xr.DataArray(zwnp, dims=zwvolume.dims, coords=zwvolume.coords)
zwda_xavg = zonal_avg(zwda, zwvolume)
xavg_dict = {'xxx': zwda_xavg.latitude.values, 'yyy': zwda_xavg.depth.values, 'ccc': zwda_xavg.values}
return xavg_dict
#
#----------------
# Initiate data2plot
#----------------
data2plot = {
'ESM': [], \
'Global': {
'∆AOU': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'reminder': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'R2': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'CI': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'volume': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}
}, \
'xavg': {
'∆AOU': {'total':[], 'deep':[], 'deep significant':[], \
'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'reminder': {'southern and atlantic significant': [], \
'southern and atlantic': []}
}
}
#----------------
# Load data
#----------------
print('Load data...')
if tperiod=='2015-2099': simu = 'ssp585'
elif tperiod=='1972-2013': simu = 'historical'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, 'rb') as file:
data2process_with_low_significant = pickle.load(file)
#
#----------------
# Loop on ESM
#----------------
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot['ESM'].append(vesm)
#----------------
# Compute volume and omask
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
fname = netcdfdir+f'{vesm}_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_sigma0 = zwds['sigma0']
if vesm == 'MPI-ESM1-2-LR' :
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask').drop(['latitude', 'longitude'])
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zwda_sigma0, zarea, omask
gc.collect()
#----------------
# Southern and atlantic significant
#----------------
print(' Southern and atlantic significant...')
zw_delta_aou_SA, zw_delta_age_SA, zw_reminder_SA = 0, 0, 0
zwvolume_SA, zwr2_SA = 0, 0
zwCI_SA = 0
zw_delta_aou_3D_SA, zw_delta_age_3D_SA, zw_reminder_3D_SA = [], [], []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
#______________
# Global
zwaou = data2process_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_significant[vesm][watermass]['integrated age trend']
zwour = data2process_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_significant[vesm][watermass]['volume']
zw_delta_age = zwour * zwage
zw_reminder = zwaou - zw_delta_age
zw_delta_aou_SA += zwaou
zw_delta_age_SA += zw_delta_age
zw_reminder_SA += zw_reminder
zwr2_SA += zwr2 * zwvolume
zwvolume_SA += zwvolume
#______________
# XAVG
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_delta_aou_3D_SA.append(zw_delta_aou_3D)
zw_delta_age_3D_SA.append(zw_delta_age_3D)
zw_reminder_3D_SA .append(zw_reminder_3D )
#______________
# Confidence interval of aou prediction
zwCI_3D = data2process_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zwCI_SA += np.nansum(zwCI_3D*(volume.values))
#
zwr2_SA = zwr2_SA / zwvolume_SA
zw_delta_aou_3D_SA = np.array(zw_delta_aou_3D_SA)
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_reminder_3D_SA = np.array(zw_reminder_3D_SA )
all_nan_mask = np.all(np.isnan(zw_delta_aou_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_aou_3D_SA, axis=0)
zw_delta_aou_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_reminder_3D_SA), axis=0)
zwnansum = np.nansum(zw_reminder_3D_SA, axis=0)
zw_reminder_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic significant'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou_SA)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder_SA)
data2plot[datatype]['R2' ][watermass].append(zwr2_SA)
data2plot[datatype]['CI' ][watermass].append(zwCI_SA)
data2plot[datatype]['volume' ][watermass].append(zwvolume_SA)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D_SA, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D_SA, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
#----------------
# Southern and atlantic including low significant water-masses
#----------------
print(' Southern and atlantic including low significant water-masses...')
zw_delta_aou_SA, zw_delta_age_SA, zw_reminder_SA = 0, 0, 0
zwvolume_SA, zwr2_SA = 0, 0
zwCI_SA = 0
zw_delta_aou_3D_SA, zw_delta_age_3D_SA, zw_reminder_3D_SA = [], [], []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
#______________
# Global
zwaou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_with_low_significant[vesm][watermass]['integrated age trend']
zwour = data2process_with_low_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_with_low_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_with_low_significant[vesm][watermass]['volume']
zw_delta_age = zwour * zwage
zw_reminder = zwaou - zw_delta_age
zw_delta_aou_SA += zwaou
zw_delta_age_SA += zw_delta_age
zw_reminder_SA += zw_reminder
zwr2_SA += zwr2 * zwvolume
zwvolume_SA += zwvolume
#______________
# XAVG
zw_pred_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_with_low_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_delta_aou_3D_SA.append(zw_delta_aou_3D)
zw_delta_age_3D_SA.append(zw_delta_age_3D)
zw_reminder_3D_SA .append(zw_reminder_3D )
#______________
# Confidence interval of aou prediction
zwCI_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zwCI_SA += np.nansum(zwCI_3D*(volume.values))
#
zwr2_SA = zwr2_SA / zwvolume_SA
zw_delta_aou_3D_SA = np.array(zw_delta_aou_3D_SA)
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_reminder_3D_SA = np.array(zw_reminder_3D_SA )
all_nan_mask = np.all(np.isnan(zw_delta_aou_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_aou_3D_SA, axis=0)
zw_delta_aou_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_reminder_3D_SA), axis=0)
zwnansum = np.nansum(zw_reminder_3D_SA, axis=0)
zw_reminder_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou_SA)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder_SA)
data2plot[datatype]['R2' ][watermass].append(zwr2_SA)
data2plot[datatype]['CI' ][watermass].append(zwCI_SA)
data2plot[datatype]['volume' ][watermass].append(zwvolume_SA)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D_SA, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D_SA, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
#----------------
# Total, deep and deep significant (only ∆AOU and volume)...
#----------------
print(' Total, deep and deep significant (only ∆AOU and volume)...')
datatype = 'Global'
# total
watermass = 'total'
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass].append(zwvolume)
# deep significant
watermass = 'deep'
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass+' significant'].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass+' significant'].append(zwvolume)
# deep including low significance
watermass = 'deep'
zw_delta_aou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
zwvolume = data2process_significant[vesm][watermass]['volume']
data2plot[datatype]['volume' ][watermass].append(zwvolume)
#
#
#----------------
# Save data2plot in pickle
#----------------
print('Save file...')
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_1972-2013.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot each watermasses¶
Prepare data2plot¶
In [14]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder')
print('## 1972-2013')
print('### Plot each watermasses')
print('#### Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
tperiod = '1972-2013'
#----------------
# Define some functions
#----------------
def compute_xavg(zwnp, zwvolume):
zwda = xr.DataArray(zwnp, dims=zwvolume.dims, coords=zwvolume.coords)
zwda_xavg = zonal_avg(zwda, zwvolume)
xavg_dict = {'xxx': zwda_xavg.latitude.values, 'yyy': zwda_xavg.depth.values, 'ccc': zwda_xavg.values}
return xavg_dict
#
#----------------
# Initiate data2plot
#----------------
data2plot = {
'ESM': [], \
'Global': {
'∆AOU': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'∆Age': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'reminder': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'R2': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'CI': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'volume': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}
}, \
'xavg': {
'∆AOU': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'∆Age': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'reminder': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'intercept': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
'residual': {'southern dense significant':[], 'southern light significant':[], \
'atlantic dense significant':[], 'atlantic light significant':[], \
'southern dense':[], 'southern light':[], \
'atlantic dense':[], 'atlantic light':[]}, \
}
}
#----------------
# Load data
#----------------
print(' Load data...')
if tperiod=='2015-2099': simu = 'ssp585'
elif tperiod=='1972-2013': simu = 'historical'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, 'rb') as file:
data2process_with_low_significant = pickle.load(file)
#
#----------------
# Loop on ESM
#----------------
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot['ESM'].append(vesm)
#----------------
# Compute volume and omask
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
fname = netcdfdir+f'{vesm}_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_sigma0 = zwds['sigma0']
if vesm == 'MPI-ESM1-2-LR' :
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask').drop(['latitude', 'longitude'])
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zwda_sigma0, zarea, omask
gc.collect()
#----------------
# Loop on water-masses
#----------------
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
print(f' {watermass}...')
#________________
# Significant water-masses
# Global
zw_delta_aou = data2process_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_significant[vesm][watermass]['integrated age trend']
zwour = data2process_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_significant[vesm][watermass]['volume']
zwCI_3D = data2process_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zw_delta_age = zwour * zwage
zw_reminder = zw_delta_aou - zw_delta_age
zwCI = np.nansum(zwCI_3D*(volume.values))
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass+' significant'].append(zw_delta_aou)
data2plot[datatype]['∆Age' ][watermass+' significant'].append(zw_delta_age)
data2plot[datatype]['reminder' ][watermass+' significant'].append(zw_reminder)
data2plot[datatype]['R2' ][watermass+' significant'].append(zwr2)
data2plot[datatype]['CI' ][watermass+' significant'].append(zwCI)
data2plot[datatype]['volume' ][watermass+' significant'].append(zwvolume)
# xavg
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_itcpt_3D = np.where(np.isnan(zw_bias_3D), np.nan, zw_itcpt)
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D, volume)
data2plot[datatype]['∆AOU'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_delta_age_3D, volume)
data2plot[datatype]['∆Age'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_reminder_3D, volume)
data2plot[datatype]['reminder'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_itcpt_3D, volume)
data2plot[datatype]['intercept'][watermass+' significant'].append(xavg)
xavg = compute_xavg(zw_bias_3D, volume)
data2plot[datatype]['residual'][watermass+' significant'].append(xavg)
#________________
# Including low significant water-masses
# Global
zw_delta_aou = data2process_with_low_significant[vesm][watermass]['integrated aou trend']
zwage = data2process_with_low_significant[vesm][watermass]['integrated age trend']
zwour = data2process_with_low_significant[vesm][watermass]['linregress']['slope']
zwr2 = data2process_with_low_significant[vesm][watermass]['linregress']['rvalue']**2
zwvolume = data2process_with_low_significant[vesm][watermass]['volume']
zwCI_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['confidence interval']['3D']['ccc']
zw_delta_age = zwour * zwage
zw_reminder = zw_delta_aou - zw_delta_age
zwCI = np.nansum(zwCI_3D*(volume.values))
datatype = 'Global'
data2plot[datatype]['∆AOU' ][watermass].append(zw_delta_aou)
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age)
data2plot[datatype]['reminder' ][watermass].append(zw_reminder)
data2plot[datatype]['R2' ][watermass].append(zwr2)
data2plot[datatype]['volume' ][watermass].append(zwvolume)
data2plot[datatype]['CI' ][watermass].append(zwCI)
# xavg
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_bias_3D = data2process_significant[vesm][watermass]['aou prediction']['error']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_reminder_3D = zw_bias_3D + zw_itcpt
zw_itcpt_3D = np.where(np.isnan(zw_bias_3D), np.nan, zw_itcpt)
datatype = 'xavg'
xavg = compute_xavg(zw_delta_aou_3D, volume)
data2plot[datatype]['∆AOU'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_reminder_3D, volume)
data2plot[datatype]['reminder'][watermass].append(xavg)
xavg = compute_xavg(zw_itcpt_3D, volume)
data2plot[datatype]['intercept'][watermass].append(xavg)
xavg = compute_xavg(zw_bias_3D, volume)
data2plot[datatype]['residual'][watermass].append(xavg)
#
#
#----------------
# Save data2plot in pickle
#----------------
print('Save file...')
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_each_watermass_1972-2013.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot¶
In [13]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Compute ∆AOU = OUR * ∆Age + intercept + residual')
print('## 1972-2013')
print('### Plot each watermasses')
print('#### Plot')
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_1972-2013.pckl'
with open(savefile, 'rb') as file:
data2plot1 = pickle.load(file)
#
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_each_watermass_1972-2013.pckl'
with open(savefile, 'rb') as file:
data2plot2 = pickle.load(file)
#
watermass='southern light significant'
#____________________
# Set figname for saving figure
figname = dirout + f'plot_AOUtrend_OURxAgetrend_reminder_'+watermass.replace(' ', '_')+'_1972-2013.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot1['ESM']
section_list = ['∆AOU', '∆Age', 'reminder']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B$'
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'reminder' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-.2, vmax=.2)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 3
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iii, vvv in enumerate(section_list):
zwdata = data2plot2['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] * zfact_section
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = '[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot1['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot1['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
error_values = np.array(data2plot2['Global']['CI'][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
# bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, yerr=error_values, **kwbar1)
#_________________
# Reminder
iii = 1
var = 'reminder'
bbb += hhh
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
xxx2 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx2, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.30*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add R2 text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.005*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot2['Global']['R2'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val:.2f}', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'$\overline{R^2}$:', ha='left', va='bottom', size=6)
#--------------------
# Add volume text
#--------------------
iii=0
ytext = ylim[0]+0.1*(ylim[1]-ylim[0])
totvolume = data2plot1['Global']['volume']['total']
for ind, val in enumerate(data2plot2['Global']['volume'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val/totvolume[ind]*100:.0f}%', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'Vol. ratio:', ha='left', va='bottom', size=6)
#--------------------
# Add share of dAge
#--------------------
iii=0
ytext = 0.005*(ylim[1]-ylim[0])
daou = data2plot2['Global']['∆AOU'][watermass]
for ind, val in enumerate(data2plot2['Global']['∆Age'][watermass]):
xtext = xxx1[ind]
zax.text( xtext, ytext, f'{val/daou[ind]*100:.0f}%', c='white', ha='center', va='bottom', size=6)
#
#--------------------
# Print share of dAge
#--------------------
print('---- dAGE/dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]/daou[ind]*100:.0f}%')
#
print('-----------------')
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {daou[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Plot with intercept and residual¶
In [19]:
%%time
%%memit -c
print(datetime.datetime.now())
print('# Compute ∆AOU = OUR * ∆Age + intercept + residual')
print('## 1972-2013')
print('### Plot each watermasses')
print('#### Plot with intercept and residual')
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_1972-2013.pckl'
with open(savefile, 'rb') as file:
data2plot1 = pickle.load(file)
#
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder_each_watermass_1972-2013.pckl'
with open(savefile, 'rb') as file:
data2plot2 = pickle.load(file)
#
watermass='atlantic light significant'
#____________________
# Set figname for saving figure
figname = dirout + f'plot_AOUtrend_OURxAgetrend_intercept_residual_'+watermass.replace(' ', '_')+'_1972-2013.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot1['ESM']
section_list = ['∆AOU', '∆Age', 'intercept', 'residual']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B$',
'intercept' : r'intercept',
'residual' : r'residual',
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'reminder' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-.2, vmax=.2)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 4
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iii, vvv in enumerate(section_list):
zwdata = data2plot2['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] * zfact_section
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = '[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot1['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot1['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
error_values = np.array(data2plot2['Global']['CI'][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
# bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, yerr=error_values, **kwbar1)
#_________________
# Reminder
iii = 1
var = 'reminder'
bbb += hhh
hhh = np.array(data2plot2['Global'][var][watermass]) * zfact_glob
xxx2 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx2, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.30*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add R2 text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.005*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot2['Global']['R2'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val:.2f}', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'$\overline{R^2}$:', ha='left', va='bottom', size=6)
#--------------------
# Add volume text
#--------------------
iii=0
ytext = ylim[0]+0.1*(ylim[1]-ylim[0])
totvolume = data2plot1['Global']['volume']['total']
for ind, val in enumerate(data2plot2['Global']['volume'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val/totvolume[ind]*100:.0f}%', ha='center', va='bottom', size=6)
#
xtext = xlim[0]+0.005*(xlim[1]-xlim[0])
zax.text(xtext, ytext, r'Vol. ratio:', ha='left', va='bottom', size=6)
#--------------------
# Add share of dAge
#--------------------
iii=0
ytext = 0.005*(ylim[1]-ylim[0])
daou = data2plot2['Global']['∆AOU'][watermass]
for ind, val in enumerate(data2plot2['Global']['∆Age'][watermass]):
xtext = xxx1[ind]
zax.text( xtext, ytext, f'{val/daou[ind]*100:.0f}%', c='white', ha='center', va='bottom', size=6)
#
#--------------------
# Print share of dAge
#--------------------
print('---- dAGE/dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]/daou[ind]*100:.0f}%')
#
print('-----------------')
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAOU ----')
daou = data2plot2['Global']['∆AOU'][watermass]
dage = data2plot2['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot2['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {daou[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
Figure ∆AOU = OUR * ∆Age + reminder with OUR from MPI-ESM1-2-LR¶
Prepare data2plot¶
In [19]:
%%time
%%memit -c
# ca. 2min
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder with OUR from MPI-ESM1-2-LR')
print('## Prepare data2plot')
print('################################')
esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
tperiod = '2015-2099'
esm_ref = 'MPI-ESM1-2-LR'
#----------------
# Del some existing variables
#----------------
print('Delete some existing variables...')
def check_and_delete_variable(variable_name):
if variable_name in globals():
del globals()[variable_name]
print(f"{variable_name} deleted")
elif variable_name in locals():
del locals()[variable_name]
print( f"{variable_name} deleted" )
else:
print( f"{variable_name} does not exist")
#
for vvv in ['data2plot', 'data2process_significant', 'data2process_with_low_significant']:
check_and_delete_variable(vvv)
#
gc.collect()
#----------------
# Define some functions
#----------------
def compute_xavg(zwnp, zwvolume):
zwda = xr.DataArray(zwnp, dims=zwvolume.dims, coords=zwvolume.coords)
zwda_xavg = zonal_avg(zwda, zwvolume)
xavg_dict = {'xxx': zwda_xavg.latitude.values, 'yyy': zwda_xavg.depth.values, 'ccc': zwda_xavg.values}
return xavg_dict
#
#----------------
# Initiate data2plot
#----------------
data2plot = {
'ESM': [], \
'Global': {
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age new': {'southern and atlantic significant': [], \
'southern and atlantic': []}
}, \
'xavg': {
'∆Age': {'southern and atlantic significant': [], \
'southern and atlantic': []}, \
'∆Age new': {'southern and atlantic significant': [], \
'southern and atlantic': []}
}
}
#----------------
# Load data
#----------------
print('Load data...')
if tperiod=='2015-2099': simu = 'ssp585'
elif tperiod=='1972-2013': simu = 'historical'
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'.pckl'
with open(savefile, 'rb') as file:
data2process_significant = pickle.load(file)
#
savefile = dirout + 'data2plot_aou_trend_vs_age_trend_'+simu+'_'+tperiod+'_include_low_significant_trend.pckl'
with open(savefile, 'rb') as file:
data2process_with_low_significant = pickle.load(file)
#
#----------------
# Loop on ESM
#----------------
for iesm, vesm in enumerate(esm_list):
print('-----------------------')
print('Prepare data for %s...'%vesm)
print('-----------------------')
data2plot['ESM'].append(vesm)
#----------------
# Compute volume and omask
#----------------
print(' Compute volume and omask...')
if vesm == 'NorESM2-LM': fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', diresgf='/mnt/reef-ns1002k-ns9034k/', variant='r1i1p1f1')
else: fname_list = get_esgf_dataset_filepaths('areacello', vesm, 'historical', variant=variant_dict[vesm],
version=version_dict['historical']['areacello'][vesm], grid=grid_dict['areacello'][vesm])
zwds = xr.open_mfdataset(fname_list, **kwopenmfds)
zwds2 = zwds['areacello'].to_dataset()
zwds2 = rename_vars_dims_coords(zwds2, rename_dict)
zwds2 = split_coords_dimensions(zwds2)
zwds2 = shift_180_lon(zwds2)
zarea = zwds2['areacello']
# FOR MPI model, invert latitude dimension
if vesm == 'MPI-ESM1-2-LR' :
zarea = zarea.isel(j=slice(None, None, -1))
#
fname = netcdfdir+f'{vesm}_historical_sigma0_average_on_1972-2013.nc'
zwds = xr.open_mfdataset(fname, **kwopenmfds)
zwda_sigma0 = zwds['sigma0']
if vesm == 'MPI-ESM1-2-LR' :
zwda_sigma0 = zwda_sigma0.isel(j=slice(None, None, -1))
#
depbounds, depthickness = compute_vertical_bounds_thickness(zwda_sigma0.depth)
omask = ~(zwda_sigma0.isnull()).rename('mask').drop(['latitude', 'longitude'])
volume = (omask * depthickness * zarea).compute()
del depbounds, depthickness, zwda_sigma0, zarea, omask
gc.collect()
#----------------
# Southern and atlantic significant
#----------------
print(' Southern and atlantic significant...')
zw_delta_age_SA = 0
zw_delta_age_3D_SA = []
zw_delta_age_SA_new = 0
zw_delta_age_3D_SA_new = []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
zwour_ref = data2process_significant[esm_ref][watermass]['linregress']['slope']
#______________
# Global
zwage = data2process_significant[vesm][watermass]['integrated age trend']
zwour = data2process_significant[vesm][watermass]['linregress']['slope']
zw_delta_age = zwour * zwage
zw_delta_age_new = zwour_ref * zwage
zw_delta_age_SA += zw_delta_age
zw_delta_age_SA_new += zw_delta_age_new
#______________
# XAVG
# Y3D_predicted = results.intercept + results.slope * X3D -> x3d = (zw_pred_3D - zw_itcpt)/zwour
# Y3D_error = Y3D - Y3D_predicted -> zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_pred_3D = data2process_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_itcpt = data2process_significant[vesm][watermass]['linregress']['intercept']
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_delta_age_3D_new = (zw_pred_3D - zw_itcpt)/zwour * zwour_ref
zw_delta_age_3D_SA .append(zw_delta_age_3D)
zw_delta_age_3D_SA_new.append(zw_delta_age_3D_new)
#
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_delta_age_3D_SA_new = np.array(zw_delta_age_3D_SA_new)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA_new), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA_new, axis=0)
zw_delta_age_3D_SA_new = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic significant'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['∆Age new'][watermass].append(zw_delta_age_SA_new)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA_new, volume)
data2plot[datatype]['∆Age new'][watermass].append(xavg)
#----------------
# Southern and atlantic including low significant water-masses
#----------------
print(' Southern and atlantic including low significant water-masses...')
zw_delta_age_SA = 0
zw_delta_age_3D_SA = []
zw_delta_age_SA_new = 0
zw_delta_age_3D_SA_new = []
for watermass in ['southern dense', 'southern light', 'atlantic dense', 'atlantic light']:
zwour_ref = data2process_with_low_significant[esm_ref][watermass]['linregress']['slope']
#______________
# Global
zwage = data2process_with_low_significant[vesm][watermass]['integrated age trend']
zwour = data2process_with_low_significant[vesm][watermass]['linregress']['slope']
zw_delta_age = zwour * zwage
zw_delta_age_new = zwour_ref * zwage
zw_delta_age_SA += zw_delta_age
zw_delta_age_SA_new += zw_delta_age_new
#______________
# XAVG
# Y3D_predicted = results.intercept + results.slope * X3D -> x3d = (zw_pred_3D - zw_itcpt)/zwour
# Y3D_error = Y3D - Y3D_predicted -> zw_delta_aou_3D = zw_pred_3D + zw_bias_3D
zw_pred_3D = data2process_with_low_significant[vesm][watermass]['aou prediction']['predicted']['3D']['ccc']
zw_itcpt = data2process_with_low_significant[vesm][watermass]['linregress']['intercept']
zw_delta_age_3D = zw_pred_3D - zw_itcpt
zw_delta_age_3D_new = (zw_pred_3D - zw_itcpt)/zwour * zwour_ref
zw_delta_age_3D_SA .append(zw_delta_age_3D)
zw_delta_age_3D_SA_new.append(zw_delta_age_3D_new)
#
zw_delta_age_3D_SA = np.array(zw_delta_age_3D_SA)
zw_delta_age_3D_SA_new = np.array(zw_delta_age_3D_SA_new)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA, axis=0)
zw_delta_age_3D_SA = np.where(all_nan_mask, np.nan, zwnansum)
all_nan_mask = np.all(np.isnan(zw_delta_age_3D_SA_new), axis=0)
zwnansum = np.nansum(zw_delta_age_3D_SA_new, axis=0)
zw_delta_age_3D_SA_new = np.where(all_nan_mask, np.nan, zwnansum)
watermass = 'southern and atlantic'
#______________
# Global
datatype = 'Global'
data2plot[datatype]['∆Age' ][watermass].append(zw_delta_age_SA)
data2plot[datatype]['∆Age new'][watermass].append(zw_delta_age_SA_new)
#______________
# XAVG
datatype = 'xavg'
xavg = compute_xavg(zw_delta_age_3D_SA, volume)
data2plot[datatype]['∆Age'][watermass].append(xavg)
xavg = compute_xavg(zw_delta_age_3D_SA_new, volume)
data2plot[datatype]['∆Age new'][watermass].append(xavg)
#
#----------------
# Save data2plot in pickle
#----------------
print('Save file...')
savefile = dirout + f'data2plot_OURxAgetrend_OUR_from_{esm_ref}.pckl'
with open(savefile, "wb") as file:
pickle.dump(data2plot, file)
#
print('File saved: '+savefile)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
Plot¶
In [38]:
%%time
%%memit -c
print('################################')
print('################################')
print(datetime.datetime.now())
print('# Figure ∆AOU = OUR * ∆Age + reminder with OUR from MPI-ESM1-2-LR')
print('## Plot')
print('################################')
#____________________
# Load data2plot
savefile = dirout + 'data2plot_AOUtrend_OURxAgetrend_reminder.pckl'
with open(savefile, 'rb') as file:
data2plot = pickle.load(file)
#
esm_ref = 'MPI-ESM1-2-LR'
savefile = dirout + f'data2plot_OURxAgetrend_OUR_from_{esm_ref}.pckl'
with open(savefile, 'rb') as file:
data2plot_new = pickle.load(file)
#
watermass='southern and atlantic significant'
#____________________
# Set figname for saving figure
figname = dirout + f'plot_AOUtrend_OURxAgetrend_with_OUR_from_{esm_ref}.png'
#--------------------
# Figure parameters
#--------------------
# esm_list = ['MPI-ESM1-2-LR', 'ACCESS-ESM1-5', 'IPSL-CM6A-LR', 'MIROC-ES2L', 'NorESM2-LM']
esm_list = data2plot['ESM']
section_list = ['∆AOU', '∆Age', '∆Age new']
zfact_glob = 1e-18 * 117/170 * 12.01 # mmol-O -> PgC
zfact_section = 1
#______________
# Labels
labels_dict = {
'Global ∆AOU': r'Global $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep ∆AOU': r'Deep $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'Deep significant ∆AOU': r'Deep significant $\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆AOU' : r'$\frac{\mathrm{dAOU}}{\mathrm{dt}}$',
'∆Age' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}} \times \frac{\mathrm{dAge}}{\mathrm{dt}}$',
'∆Age new' : r'$S_{\mathrm{\Delta age}}^{\mathrm{\Delta AOU}}$ from '+esm_name_dict[esm_ref],
# 'reminder' : r'$\mathrm{Age} \times \frac{\mathrm{dOUR}}{\mathrm{dt}}$',
'reminder' : r'$B$'
}
#______________
# barplot colors
barplot_color_dict = {
'Global ∆AOU': 'lightgrey',
'∆AOU': 'grey',
'∆Age' : cmcrameri.cm.batlowS.colors[0],
'∆Age new' : cmcrameri.cm.batlowS.colors[1]
}
#______________
# barplot kwargs
kwbar = dict(linewidth=0.5, align='center')
kwbar1 = dict(linewidth=.5, align='center', edgecolor='k')
#______________
# Sections axis labeling and limits
xlabel = 'Latitude [Deg. N]'
ylabel = 'Depth [m]'
xlim = (-90, 90)
ylim = (6000, -10)
#______________
# Section cmap
a = cmcrameri.cm.vik
colors = a(np.linspace(0,1,10+2))
newcmap = ListedColormap(colors[1:-1])
newcmap.set_over(colors[-1])
newcmap.set_under(colors[0])
newcmap.set_bad("none")
#______________
# Section pcm kwargs
kwpcm = dict(cmap=newcmap, vmin=-.2, vmax=.2)
#______________
# panel labeling
import string
alphabet = list(string.ascii_lowercase)
alphabet_reverse = alphabet[::-1]
# Create a list of double letter combinations (aa, ab, ..., zz)
double_letters = [a + b for a in string.ascii_lowercase for b in string.ascii_lowercase]
extended_alphabet = alphabet + double_letters
extended_alphabet_reverse = extended_alphabet[::-1]
# Pop one now to save a) for the barplot at the top
extended_alphabet_reverse.pop()
#______________
# Set figure
ncol = 3
nrow = len(esm_list)
fwidth_cm = 4
fheight_cm = 3
fsize = (fwidth_cm*cm2in*ncol, fheight_cm*cm2in*nrow) # width, height
fig, ax = plt.subplots(nrows=nrow, ncols=ncol, figsize=fsize, sharex='col', sharey='row')
#--------------------
# Plot sections
#--------------------
pcm = np.zeros_like(ax)
for iesm, vesm in enumerate(esm_list):
for iii, vvv in enumerate(section_list):
if vvv=='∆Age new': zwdata = data2plot_new['xavg'][vvv][watermass][iesm]
else: zwdata = data2plot['xavg'][vvv][watermass][iesm]
zax = ax[iesm, iii]
xxx = zwdata['xxx']
yyy = zwdata['yyy']
ccc = zwdata['ccc'] * zfact_section
# ccc = np.where(ccc!=0, ccc, np.nan)
zwpcm = zax.pcolormesh(xxx, yyy, ccc, **kwpcm)
pcm[iesm, iii] = zwpcm
zax.set_ylim(ylim)
zax.set_xlim(xlim)
zwtxt = extended_alphabet_reverse.pop() + f') '
zax.text(.02, .98, zwtxt, transform=zax.transAxes, ha='left', va='top')
#
#
#--------------------
# Decorate sections
#--------------------
fig.tight_layout()
for iesm, vesm in enumerate(esm_list) :
zax = ax[iesm, 0]
zax.text(-.2, 1.2, esm_name_dict[vesm], transform=zax.transAxes, ha='right', va='top', rotation=30.)
#
for zax in ax[:, 0]: zax.set_ylabel(ylabel)
for zax in ax[-1, :]: zax.set_xlabel(xlabel)
for iii, vvv in enumerate(section_list):
zax = ax[0, iii]
zax.text(.5, 1.2, labels_dict[vvv], transform=zax.transAxes, ha='center', va='bottom')
#
#---------------------
# Colorbar
#---------------------
zw1 = ax[-1, 0].get_position()
zw2 = ax[-1, -1].get_position()
nx0 = zw1.x0 + .5*zw1.width
ny0 = zw2.y0 - .8*zw1.height
nw = zw2.x0 + .5*zw2.width - nx0
nh = 0.05*nw
cax = fig.add_axes([nx0, ny0, nw, nh])
cbar = fig.colorbar(pcm.flat[0], cax=cax, orientation='horizontal', \
ticklocation='bottom', extend='both')
clabel = '[mmol$\,$O$_2$ m$^{-3}$ yr$^{-1}$]'
cbar.set_label(clabel)
#--------------------
# Add barplot
#--------------------
zw1 = ax[0, 0].get_position()
zw2 = ax[0, -1].get_position()
nx0 = zw1.x0
ny0 = zw1.y1 + .8*zw1.height
nw = zw2.x1 - nx0
nh = 1.5*zw1.height
zax = fig.add_axes([nx0, ny0, nw, nh])
#_________________
# Get x-ticks and xxx and www0 and www1
xxx = np.arange(len(esm_list))
www0 = 0.95
www1 = 0.7
#_________________
# ∆AOU
var = '∆AOU'
hhh = np.array(data2plot['Global'][var]['total']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Global ∆AOU', color=barplot_color_dict['Global '+var], edgecolor=barplot_color_dict['Global '+var], **kwbar)
hhh = np.array(data2plot['Global'][var]['deep']) * zfact_glob
zax.bar(xxx, hhh, width=www0, label='Deep ∆AOU', color='none', edgecolor='k', **kwbar)
# hhh = np.array(data2plot['Global'][var]['deep significant']) * zfact_glob
# zax.bar(xxx, hhh, width=www0, color='none', label='Deep significant ∆AOU', linewidth=.5, ls='--', edgecolor='red', align='center')
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
zax.bar(xxx, hhh, width=www1, label=var, color=barplot_color_dict[var], edgecolor=barplot_color_dict[var], **kwbar)
nnn = 2
www2=www1/(nnn+1)
#_________________
# ∆Age
iii = 0
var = '∆Age'
hhh = np.array(data2plot['Global'][var][watermass]) * zfact_glob
# error_values = np.array(data2plot['Global']['CI'][watermass]) * zfact_glob
bbb = 0*hhh
xxx1 = xxx-(nnn-(1+2*iii))*www2/2
bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
# bars = zax.bar(xxx1, hhh, width=www2, color=barplot_color_dict[var], label=var, yerr=error_values, ecolor='white', **kwbar1)
#_________________
# ∆Age new
iii = 1
var = '∆Age new'
bbb = 0*hhh
hhh = np.array(data2plot_new['Global'][var][watermass]) * zfact_glob
xxx2 = xxx-(nnn-(1+2*iii))*www2/2
zax.bar(xxx2, hhh, bottom=bbb, width=www2, color=barplot_color_dict[var], label=var, **kwbar1)
#--------------------
# Decorate barplot
#--------------------
zax.axhline(0, c='k', lw=.5)
#____________________
# Set labels and title
ttl = 'a) Spatially integrated trends'
zax.set_title(ttl, loc='left')
zax.set_ylabel(r'[Pg$\,$C yr$^{-1}$]')
x_tick_labels = [esm_name_dict[vesm] for vesm in esm_list]
zax.set_xticks(xxx)
zax.set_xticklabels(x_tick_labels)
#____________________
# Adjust axis limits
ylim = zax.get_ylim()
dylim = 0.12*(ylim[1]-ylim[0])
ylim2 = (ylim[0]-dylim, ylim[1]+dylim)
zax.set_ylim(ylim2)
xlim = zax.get_xlim()
dxlim = 0.05*(xlim[1]-xlim[0])
xlim2 = (xlim[0]-dxlim, xlim[1])
zax.set_xlim(xlim2)
#____________________
# legend
aaa=zax.get_legend_handles_labels()
handles = aaa[0]
labels = [labels_dict[vvv] for vvv in aaa[1]]
zax.legend(handles, labels, frameon=False, handletextpad=1, ncol=2,\
loc='upper left', bbox_to_anchor=(0, 1))
#--------------------
# Add ∆Age text
#--------------------
ylim = zax.get_ylim()
xlim = zax.get_xlim()
iii=0
ytext = ylim[0]+0.02*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot['Global']['∆Age'][watermass]):
xtext = xxx[ind]
zax.text( xtext, ytext, f'{val*zfact_glob:.2f}', ha='right', va='bottom', \
size=6, c='white', \
bbox=dict(facecolor=barplot_color_dict['∆Age'], edgecolor='none', boxstyle='round,pad=0.1'))
#
#--------------------
# Add ∆Age new text
#--------------------
iii=0
# ytext = ylim[0]+0.01*(ylim[1]-ylim[0])
for ind, val in enumerate(data2plot_new['Global']['∆Age new'][watermass]):
xtext = xxx[ind]
zax.text(xtext, ytext, f'{val*zfact_glob:.2f}', ha='left', va='bottom', \
size=6, c='k', \
bbox=dict(facecolor=barplot_color_dict['∆Age new'], edgecolor='none', boxstyle='round,pad=0.1'))
#
#--------------------
# Add change of dAge
#--------------------
iii=0
# ytext = 0.005*(ylim[1]-ylim[0])
dage = data2plot['Global']['∆Age'][watermass]
for ind, val in enumerate(data2plot_new['Global']['∆Age new'][watermass]):
xtext = xxx2[ind]
vvv = (val - dage[ind])/dage[ind]*100
if vvv>0:txt = fr'+{vvv:.0f}%'
elif vvv<0: txt=fr'-{np.abs(vvv):.0f}%'
else: txt=''
zax.text( xtext, val*zfact_glob, txt, c='white', ha='center', va='bottom', size=6)
# zax.text( xtext, ytext, fr'{val/daou[ind]*100:.0f}$\pm${np.ceil(error_values[ind]/daou[ind]*100):.0f}%', \
# c='white', ha='center', va='bottom', size=6)
#
#--------------------
# Print dAOU, dAge
#--------------------
print('---- dAGE, dAGE new ----')
dage = data2plot['Global']['∆Age'][watermass]
dagenew = data2plot_new['Global']['∆Age new'][watermass]
for ind, val in enumerate(data2plot['ESM']):
print(f'{val}: {dage[ind]*zfact_glob:.2f}, {dagenew[ind]*zfact_glob:.2f} [PgC/yr]')
#
print('-----------------')
#--------------------
# Save figure
#--------------------
print('Save figure...')
fig.savefig(figname, bbox_inches='tight')
print('Figure saved: '+figname)
print('################################')
print('Done: '+str(datetime.datetime.now()))
print('################################')
print('################################')
In [ ]: